Data Transformers¶

Keras support many types of input and output data formats, including:

Multiple inputs
Multiple outputs
Higher-dimensional tensors

In this notebook, we explore how to reconcile this functionality with the sklearn ecosystem via SciKeras data transformer interface.

Table of contents¶

1. Setup
2. Data transformer interface
- 2.1 get_metadata method
3. Multiple outputs
4. Multiple inputs
5. Multidimensional inputs with MNIST dataset
- 5.1 Define Keras Model
- 5.2 Test

1. Setup¶

[1]:

try:
    import scikeras
except ImportError:
    !python -m pip install scikeras[tensorflow]

Silence TensorFlow warnings to keep output succint.

[2]:

import warnings
from tensorflow import get_logger
get_logger().setLevel('ERROR')
warnings.filterwarnings("ignore", message="Setting the random state for TF")

[3]:

import numpy as np
from scikeras.wrappers import KerasClassifier, KerasRegressor
import keras

2. Data transformer interface¶

SciKeras enables advanced Keras use cases by providing an interface to convert sklearn compliant data to whatever format your Keras model requires within SciKeras, right before passing said data to the Keras model.

This interface is implemented in the form of two sklearn transformers, one for the features (X) and one for the target (y). SciKeras loads these transformers via the target_encoder and feature_encoder methods.

By default, SciKeras implements target_encoder for both KerasClassifier and KerasRegressor to facilitate common types of tasks in sklearn. The default implementations are scikeras.utils.transformers.ClassifierLabelEncoder and scikeras.utils.transformers.RegressorTargetEncoder for KerasClassifier and KerasRegressor respectively. Information on the types of tasks that these default transformers are able to perform can be found in the SciKeras docs.

Below is an outline of the inner workings of the data transfomer interfaces to help understand when they are called:

[4]:

if False:  # avoid executing pseudocode
    from scikeras.utils.transformers import (
        ClassifierLabelEncoder,
        RegressorTargetEncoder,
    )


    class BaseWrapper:
        def fit(self, X, y):
            self.target_encoder_ = self.target_encoder
            self.feature_encoder_ = self.feature_encoder
            y = self.target_encoder_.fit_transform(y)
            X = self.feature_encoder_.fit_transform(X)
            self.model_.fit(X, y)
            return self

        def predict(self, X):
            X = self.feature_encoder_.transform(X)
            y_pred = self.model_.predict(X)
            return self.target_encoder_.inverse_transform(y_pred)

    class KerasClassifier(BaseWrapper):

        @property
        def target_encoder(self):
            return ClassifierLabelEncoder(loss=self.loss)

        def predict_proba(self, X):
            X = self.feature_encoder_.transform(X)
            y_pred = self.model_.predict(X)
            return self.target_encoder_.inverse_transform(y_pred, return_proba=True)


    class KerasRegressor(BaseWrapper):

        @property
        def target_encoder(self):
            return RegressorTargetEncoder()

To substitute your own data transformation routine, you must subclass the wrappers and override one of the encoder defining functions. You will have access to all attributes of the wrappers, and you can pass these to your transformer, like we do above with loss.

[5]:

from sklearn.base import BaseEstimator, TransformerMixin

[6]:

if False:  # avoid executing pseudocode

    class MultiOutputTransformer(BaseEstimator, TransformerMixin):
        ...


    class MultiOutputClassifier(KerasClassifier):

        @property
        def target_encoder(self):
            return MultiOutputTransformer(...)

2.1 get_metadata method¶

SciKeras recognized an optional get_metadata on the transformers. get_metadata is expected to return a dicionary of with key strings and arbitrary values. SciKeras will set add these items to the wrappers namespace and make them available to your model building function via the meta keyword argument:

[7]:

if False:  # avoid executing pseudocode

    class MultiOutputTransformer(BaseEstimator, TransformerMixin):
        def get_metadata(self):
            return {"my_param_": "foobarbaz"}


    class MultiOutputClassifier(KerasClassifier):

        @property
        def target_encoder(self):
            return MultiOutputTransformer(...)


    def get_model(meta):
        print(f"Got: {meta['my_param_']}")


    clf = MultiOutputClassifier(model=get_model)
    clf.fit(X, y)  # Got: foobarbaz
    print(clf.my_param_)  # foobarbaz

3. Multiple outputs¶

Keras makes it straight forward to define models with multiple outputs, that is a Model with multiple sets of fully-connected heads at the end of the network. This functionality is only available in the Functional Model and subclassed Model definition modes, and is not available when using Sequential.

In practice, the main thing about Keras models with multiple outputs that you need to know as a SciKeras user is that Keras expects X or y to be a list of arrays/tensors, with one array/tensor for each input/output.

Note that “multiple outputs” in Keras has a slightly different meaning than “multiple outputs” in sklearn. Many tasks that would be considered “multiple output” tasks in sklearn can be mapped to a single “output” in Keras with multiple units. This notebook specifically focuses on the cases that require multiple distinct Keras outputs.

3.1 Define Keras Model¶

Here we define a simple perceptron that has two outputs, corresponding to one binary classification taks and one multiclass classification task. For example, one output might be “image has car” (binary) and the other might be “color of car in image” (multiclass).

[8]:

def get_clf_model(meta):
    inp = keras.layers.Input(shape=(meta["n_features_in_"],))
    x1 = keras.layers.Dense(100, activation="relu")(inp)
    out_bin = keras.layers.Dense(1, activation="sigmoid")(x1)
    out_cat = keras.layers.Dense(meta["n_classes_"][1], activation="softmax")(x1)
    model = keras.Model(inputs=inp, outputs=[out_bin, out_cat])
    model.compile(
        loss=["binary_crossentropy", "sparse_categorical_crossentropy"]
    )
    return model

Let’s test that this model works with the kind of inputs and outputs we expect.

[9]:

X = np.random.random(size=(100, 10))
y_bin = np.random.randint(0, 2, size=(100,))
y_cat = np.random.randint(0, 5, size=(100, ))
y = [y_bin, y_cat]

# build mock meta
meta = {
    "n_features_in_": 10,
    "n_classes_": [2, 5]  # note that we made this a list, one for each output
}

model = get_clf_model(meta=meta)

model.fit(X, y, verbose=0)
y_pred = model.predict(X)

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[10]:

print(y_pred[0][:2, :])

[[0.56231505]
 [0.5747259 ]]

[11]:

print(y_pred[1][:2, :])

[[0.18681137 0.16957541 0.17409481 0.22915001 0.24036846]
 [0.1815978  0.18473804 0.16271234 0.20414665 0.26680514]]

As you can see, our predict output is also a list of arrays, except it contains probabilities instead of the class predictions.

Our data transormer’s job will be to convert from a single numpy array (which is what the sklearn ecosystem works with) to the list of arrays and then back. Additionally, for classifiers, we will want to be able to convert probabilities to class predictions.

We will structure our data on the sklearn side by column-stacking our list of arrays. This works well in this case since we have the same number of datapoints in each array.

3.2 Define output data transformer¶

Let’s go ahead and protoype this data transformer:

[12]:

from typing import List

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder


class MultiOutputTransformer(BaseEstimator, TransformerMixin):

    def fit(self, y):
        y_bin, y_cat = y[:, 0], y[:, 1]
        # Create internal encoders to ensure labels are 0, 1, 2...
        self.bin_encoder_ = LabelEncoder()
        self.cat_encoder_ = LabelEncoder()
        # Fit them to the input data
        self.bin_encoder_.fit(y_bin)
        self.cat_encoder_.fit(y_cat)
        # Save the number of classes
        self.n_classes_ = [
            self.bin_encoder_.classes_.size,
            self.cat_encoder_.classes_.size,
        ]
        # Save number of expected outputs in the Keras model
        # SciKeras will automatically use this to do error-checking
        self.n_outputs_expected_ = 2
        return self

    def transform(self, y: np.ndarray) -> List[np.ndarray]:
        y_bin, y_cat = y[:, 0], y[:, 1]
        # Apply transformers to input array
        y_bin = self.bin_encoder_.transform(y_bin)
        y_cat = self.cat_encoder_.transform(y_cat)
        # Split the data into a list
        return [y_bin, y_cat]

    def inverse_transform(self, y: List[np.ndarray], return_proba: bool = False) -> np.ndarray:
        y_pred_proba = y  # rename for clarity, what Keras gives us are probs
        if return_proba:
            return np.column_stack(y_pred_proba, axis=1)
        # Get class predictions from probabilities
        y_pred_bin = (y_pred_proba[0] > 0.5).astype(int).reshape(-1, )
        y_pred_cat = np.argmax(y_pred_proba[1], axis=1)
        # Pass back through LabelEncoder
        y_pred_bin = self.bin_encoder_.inverse_transform(y_pred_bin)
        y_pred_cat = self.cat_encoder_.inverse_transform(y_pred_cat)
        return np.column_stack([y_pred_bin, y_pred_cat])

    def get_metadata(self):
        return {
            "n_classes_": self.n_classes_,
            "n_outputs_expected_": self.n_outputs_expected_,
        }

Note that in addition to the usual transform and inverse_transform methods, we implement the get_metadata method to return the n_classes_ attribute.

Lets test our transformer with the same dataset we previoulsy used to test our model:

[13]:

tf = MultiOutputTransformer()

y_sklearn = np.column_stack(y)

y_keras = tf.fit_transform(y_sklearn)
print("`y`, as will be passed to Keras:")
print([y_keras[0][:4], y_keras[1][:4]])

`y`, as will be passed to Keras:
[array([0, 0, 0, 0]), array([0, 1, 0, 4])]

[14]:

y_pred_sklearn = tf.inverse_transform(y_pred)
print("`y_pred`, as will be returned to sklearn:")
y_pred_sklearn[:5]

`y_pred`, as will be returned to sklearn:

[14]:

array([[1, 4],
       [1, 4],
       [1, 4],
       [1, 4],
       [1, 4]])

[15]:

print(f"metadata = {tf.get_metadata()}")

metadata = {'n_classes_': [2, 5], 'n_outputs_expected_': 2}

Since this looks good, we move on to integrating our transformer into our classifier.

[16]:

from sklearn.metrics import accuracy_score


class MultiOutputClassifier(KerasClassifier):

    @property
    def target_encoder(self):
        return MultiOutputTransformer()

    @staticmethod
    def scorer(y_true, y_pred, **kwargs):
        y_bin, y_cat = y_true[:, 0], y_true[:, 1]
        y_pred_bin, y_pred_cat = y_pred[:, 0], y_pred[:, 1]
        # Keras by default uses the mean of losses of each outputs, so here we do the same
        return np.mean([accuracy_score(y_bin, y_pred_bin), accuracy_score(y_cat, y_pred_cat)])

3.3 Test classifier¶

[17]:

from sklearn.preprocessing import StandardScaler

# Use labels as features, just to make sure we can learn correctly
X = y_sklearn
X = StandardScaler().fit_transform(X)

[18]:

clf = MultiOutputClassifier(model=get_clf_model, verbose=0, random_state=0)

clf.fit(X, y_sklearn).score(X, y_sklearn)

[18]:

0.52

4. Multiple inputs¶

The process for multiple inputs is similar, but instead of overriding the transformer in target_encoder we override feature_encoder.

[19]:

if False:
    from sklearn.base import BaseEstimator, TransformerMixin


    class MultiInputTransformer(BaseEstimator, TransformerMixin):
        ...


    class MultiInputClassifier(KerasClassifier):

        @property
        def feature_encoder(self):
            return MultiInputTransformer(...)

4.1 Define Keras Model¶

Let’s define a Keras regression Model with 2 inputs:

[20]:

def get_reg_model():

    inp1 = keras.layers.Input(shape=(1, ))
    inp2 = keras.layers.Input(shape=(1, ))

    x1 = keras.layers.Dense(100, activation="relu")(inp1)
    x2 = keras.layers.Dense(50, activation="relu")(inp2)

    concat = keras.layers.Concatenate(axis=-1)([x1, x2])

    out = keras.layers.Dense(1)(concat)

    model = keras.Model(inputs=[inp1, inp2], outputs=out)
    model.compile(loss="mse")

    return model

And test it with a small mock dataset:

[21]:

X = np.random.random(size=(100, 2))
y = np.sum(X, axis=1)
X = np.split(X, 2, axis=1)

model = get_reg_model()

model.fit(X, y, verbose=0)
y_pred = model.predict(X).squeeze()

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[22]:

from sklearn.metrics import r2_score

r2_score(y, y_pred)

[22]:

-4.829618096235808

Having verified that our model builds without errors and accepts the inputs types we expect, we move onto integrating a transformer into our SciKeras model.

4.2 Define data transformer¶

Just like for overriding target_encoder, we just need to define a sklearn transformer and drop it into our SciKeras wrapper. Since we hardcoded the input shapes into our model and do not rely on any transformer-generated metadata, we can simply use sklearn.preprocessing.FunctionTransformer:

[23]:

from sklearn.preprocessing import FunctionTransformer


class MultiInputRegressor(KerasRegressor):

    @property
    def feature_encoder(self):
        return FunctionTransformer(
            func=lambda X: [X[:, 0], X[:, 1]],
        )

Note that we did not implement inverse_transform (that is, we did not pass an inverse_func argument to FunctionTransformer) because features are never converted back to their original form.

4.3 Test regressor¶

[24]:

reg = MultiInputRegressor(model=get_reg_model, verbose=0, random_state=0)

X_sklearn = np.column_stack(X)

reg.fit(X_sklearn, y).score(X_sklearn, y)

[24]:

-3.9148403122153184

5. Multidimensional inputs with MNIST dataset¶

In this example, we look at how we can use SciKeras to process the MNIST dataset. The dataset is composed of 60,000 images of digits, each of which is a 2D 28x28 image.

The dataset and Keras Model architecture used come from a Keras example. It may be beneficial to understand the Keras model by reviewing that example first.

[25]:

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train.shape

[25]:

(60000, 28, 28)

The outputs (labels) are numbers 0-9:

[26]:

print(y_train.shape)
print(np.unique(y_train))

(60000,)
[0 1 2 3 4 5 6 7 8 9]

First, we will “flatten” the data into an array of shape (n_samples, 28*28) (i.e. a 2D array). This will allow us to use sklearn ecosystem utilities, for example, sklearn.preprocessing.MinMaxScaler.

[27]:

from sklearn.preprocessing import MinMaxScaler

n_samples_train = x_train.shape[0]
n_samples_test = x_test.shape[0]

x_train = x_train.reshape((n_samples_train, -1))
x_test = x_test.reshape((n_samples_test, -1))
x_train = MinMaxScaler().fit_transform(x_train)
x_test = MinMaxScaler().fit_transform(x_test)

[28]:

print(x_train.shape[1:])  # 784 = 28*28

(784,)

[29]:

print(np.min(x_train), np.max(x_train))  # scaled 0-1

0.0 1.0

Of course, in this case, we could have just as easily used numpy functions to scale our data, but we use MinMaxScaler to demonstrate use of the sklearn ecosystem.

5.1 Define Keras Model¶

Next we will define our Keras model (adapted from keras.io):

[30]:

num_classes = 10
input_shape = (28, 28, 1)


def get_model(meta):
    model = keras.Sequential(
        [
            keras.Input(input_shape),
            keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
            keras.layers.MaxPooling2D(pool_size=(2, 2)),
            keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
            keras.layers.MaxPooling2D(pool_size=(2, 2)),
            keras.layers.Flatten(),
            keras.layers.Dropout(0.5),
            keras.layers.Dense(num_classes, activation="softmax"),
        ]
    )
    model.compile(
        loss="sparse_categorical_crossentropy"
    )
    return model

Now let’s define a transformer that we will use to reshape our input from the sklearn shape ((n_samples, 784)) to the Keras shape (which we will be (n_samples, 28, 28, 1)).

[31]:

class MultiDimensionalClassifier(KerasClassifier):

    @property
    def feature_encoder(self):
        return FunctionTransformer(
            func=lambda X: X.reshape(X.shape[0], *input_shape),
        )

[32]:

clf = MultiDimensionalClassifier(
    model=get_model,
    epochs=10,
    batch_size=128,
    validation_split=0.1,
    random_state=0,
)

5.2 Test¶

Train and score the model (this takes some time)

[33]:

clf.fit(x_train, y_train)

Epoch 1/10

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textbf{101/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{18s} 57ms/step - loss: 1.3017: end{sphinxVerbatim}

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textbf{111/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2534: end{sphinxVerbatim}

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textbf{115/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2354: end{sphinxVerbatim}

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textbf{119/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2181: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2139: end{sphinxVerbatim}

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2097: end{sphinxVerbatim}

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textbf{122/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2056: end{sphinxVerbatim}

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textbf{123/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{17s} 57ms/step - loss: 1.2015: end{sphinxVerbatim}

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textbf{125/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1934: end{sphinxVerbatim}

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textbf{127/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1855: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1816: end{sphinxVerbatim}

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textbf{130/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1739: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1701: end{sphinxVerbatim}

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textbf{132/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1664: end{sphinxVerbatim}

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textbf{133/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1626: end{sphinxVerbatim}

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textbf{134/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1590: end{sphinxVerbatim}

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textbf{135/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 57ms/step - loss: 1.1553: end{sphinxVerbatim}

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textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 54ms/step - loss: 0.6962: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 54ms/step - loss: 0.6962

398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 54ms/step - loss: 0.6954: </pre>
textbf{398/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 54ms/step - loss: 0.6954: end{sphinxVerbatim}

[1m398/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 54ms/step - loss: 0.6954

399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 54ms/step - loss: 0.6945: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 54ms/step - loss: 0.6945: end{sphinxVerbatim}

[1m399/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 54ms/step - loss: 0.6945

401/422 ━━━━━━━━━━━━━━━━━━━━ 1s 54ms/step - loss: 0.6927: </pre>
textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 54ms/step - loss: 0.6927: end{sphinxVerbatim}

[1m401/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 54ms/step - loss: 0.6927

402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 54ms/step - loss: 0.6919: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 54ms/step - loss: 0.6919: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 54ms/step - loss: 0.6919

403/422 ━━━━━━━━━━━━━━━━━━━━ 1s 54ms/step - loss: 0.6910: </pre>
textbf{403/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 54ms/step - loss: 0.6910: end{sphinxVerbatim}

[1m403/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 54ms/step - loss: 0.6910

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6901: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6901: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6901

405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6893: </pre>
textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6893: end{sphinxVerbatim}

[1m405/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6893

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6884: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6884: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6884

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6875: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6875: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6875

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6867: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6867: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6867

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6858: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6858: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6858

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6850: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6850: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6850

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.6842: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.6842: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.6842

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6833: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6833: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6833

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6825: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6825: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6825

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6816: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6816: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6816

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6808: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6808: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6808

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6800: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6800: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6800

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6792: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6792: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6792

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6783: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6783: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6783

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6775: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6775: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6775

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6767: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6767: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6767

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6759: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 55ms/step - loss: 0.6759: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 55ms/step - loss: 0.6759

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 55ms/step - loss: 0.6751: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 55ms/step - loss: 0.6751: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 55ms/step - loss: 0.6751

422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 58ms/step - loss: 0.6743 - val_loss: 0.0757: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 58ms/step - loss: 0.6743 - val_loss: 0.0757: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 58ms/step - loss: 0.6743 - val_loss: 0.0757

Epoch 2/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 1:56:36 17s/step - loss: 0.2275: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{1:56:36} 17s/step - loss: 0.2275: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:56:36[0m 17s/step - loss: 0.2275

 2/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - loss: 0.1978: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 64ms/step - loss: 0.1978: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 64ms/step - loss: 0.1978

 3/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - loss: 0.1889: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 63ms/step - loss: 0.1889: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 63ms/step - loss: 0.1889

 4/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - loss: 0.1796: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 64ms/step - loss: 0.1796: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 64ms/step - loss: 0.1796

 6/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.1711: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.1711: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.1711

 7/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.1668: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.1668: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.1668

 8/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.1648: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.1648: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.1648

 9/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.1628: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.1628: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.1628

 10/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.1610: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.1610: end{sphinxVerbatim}

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textbf{255/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{9s} 55ms/step - loss: 0.1277: end{sphinxVerbatim}

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textbf{371/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1230: end{sphinxVerbatim}

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textbf{372/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1230: end{sphinxVerbatim}

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textbf{373/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1230: end{sphinxVerbatim}

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textbf{375/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1229: end{sphinxVerbatim}

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textbf{376/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1229: end{sphinxVerbatim}

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textbf{377/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1228: end{sphinxVerbatim}

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textbf{378/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1228: end{sphinxVerbatim}

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textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 53ms/step - loss: 0.1228: end{sphinxVerbatim}

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textbf{380/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 53ms/step - loss: 0.1227: end{sphinxVerbatim}

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textbf{382/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 53ms/step - loss: 0.1227: end{sphinxVerbatim}

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383/422 ━━━━━━━━━━━━━━━━━━━━ 2s 53ms/step - loss: 0.1226: </pre>
textbf{383/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 53ms/step - loss: 0.1226: end{sphinxVerbatim}

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384/422 ━━━━━━━━━━━━━━━━━━━━ 2s 53ms/step - loss: 0.1226: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 53ms/step - loss: 0.1226: end{sphinxVerbatim}

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385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1225: </pre>
textbf{385/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1225: end{sphinxVerbatim}

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textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1225: end{sphinxVerbatim}

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387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1225: </pre>
textbf{387/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1225: end{sphinxVerbatim}

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textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1224: end{sphinxVerbatim}

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390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1224: </pre>
textbf{390/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1224: end{sphinxVerbatim}

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391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1223: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1223: end{sphinxVerbatim}

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392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1223: </pre>
textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1223: end{sphinxVerbatim}

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393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1223: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1223: end{sphinxVerbatim}

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textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1222: end{sphinxVerbatim}

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textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1222: end{sphinxVerbatim}

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textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1222: end{sphinxVerbatim}

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397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1221: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1221: end{sphinxVerbatim}

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398/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1221: </pre>
textbf{398/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1221: end{sphinxVerbatim}

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400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 53ms/step - loss: 0.1220: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 53ms/step - loss: 0.1220: end{sphinxVerbatim}

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textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 53ms/step - loss: 0.1220: end{sphinxVerbatim}

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textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 53ms/step - loss: 0.1219: end{sphinxVerbatim}

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textbf{403/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 53ms/step - loss: 0.1219: end{sphinxVerbatim}

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textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1219: end{sphinxVerbatim}

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textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1218: end{sphinxVerbatim}

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textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1218: end{sphinxVerbatim}

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408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1217: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1217: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1217

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1217: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1217: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1217

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1217: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1217: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1217

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1216: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1216: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1216

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1216: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1216: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1216

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1215: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1215: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1215

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1215: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1215: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1215

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1214: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1214: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1214

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1214: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1214: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1214

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1214: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1214: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1214

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 53ms/step - loss: 0.1213: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 53ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 53ms/step - loss: 0.1213

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.1213: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.1213

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.1213: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 54ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 54ms/step - loss: 0.1213

422/422 ━━━━━━━━━━━━━━━━━━━━ 40s 56ms/step - loss: 0.1212 - val_loss: 0.0552: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{40s} 56ms/step - loss: 0.1212 - val_loss: 0.0552: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 56ms/step - loss: 0.1212 - val_loss: 0.0552

Epoch 3/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 2:02:55 18s/step - loss: 0.1874: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{2:02:55} 18s/step - loss: 0.1874: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:02:55[0m 18s/step - loss: 0.1874

 3/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.1382: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.1382: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.1382

 5/422 ━━━━━━━━━━━━━━━━━━━━ 15s 38ms/step - loss: 0.1274: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15s} 38ms/step - loss: 0.1274: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 38ms/step - loss: 0.1274

 7/422 ━━━━━━━━━━━━━━━━━━━━ 15s 37ms/step - loss: 0.1213: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15s} 37ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 37ms/step - loss: 0.1213

 8/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.1186: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.1186: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.1186

 10/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.1139: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.1139: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.1139

 12/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.1094: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.1094: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.1094

 14/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.1056: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.1056: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.1056

 16/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.1028: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.1028: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.1028

 18/422 ━━━━━━━━━━━━━━━━━━━━ 15s 40ms/step - loss: 0.1016: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15s} 40ms/step - loss: 0.1016: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 40ms/step - loss: 0.1016

 20/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.1008: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.1008: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.1008

 22/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.1002: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.1002: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.1002

 24/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.0996: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.0996: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.0996

 26/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.0992: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.0992: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.0992

 28/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.0988: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.0988: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.0988

 30/422 ━━━━━━━━━━━━━━━━━━━━ 15s 39ms/step - loss: 0.0985: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 39ms/step - loss: 0.0985: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 39ms/step - loss: 0.0985

 32/422 ━━━━━━━━━━━━━━━━━━━━ 15s 38ms/step - loss: 0.0983: </pre>
textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 38ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 38ms/step - loss: 0.0983

 34/422 ━━━━━━━━━━━━━━━━━━━━ 14s 38ms/step - loss: 0.0983: </pre>
textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{14s} 38ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 38ms/step - loss: 0.0983

 36/422 ━━━━━━━━━━━━━━━━━━━━ 14s 38ms/step - loss: 0.0983: </pre>
textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{14s} 38ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 36/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 38ms/step - loss: 0.0983

 37/422 ━━━━━━━━━━━━━━━━━━━━ 14s 39ms/step - loss: 0.0984: </pre>
textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{14s} 39ms/step - loss: 0.0984: end{sphinxVerbatim}

[1m 37/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 39ms/step - loss: 0.0984

 39/422 ━━━━━━━━━━━━━━━━━━━━ 14s 39ms/step - loss: 0.0988: </pre>
textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{14s} 39ms/step - loss: 0.0988: end{sphinxVerbatim}

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textbf{ 91/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 43ms/step - loss: 0.0977: end{sphinxVerbatim}

[1m 91/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - loss: 0.0977

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textbf{ 92/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 43ms/step - loss: 0.0977: end{sphinxVerbatim}

[1m 92/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - loss: 0.0977

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textbf{ 93/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 43ms/step - loss: 0.0976: end{sphinxVerbatim}

[1m 93/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - loss: 0.0976

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textbf{ 94/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 43ms/step - loss: 0.0976: end{sphinxVerbatim}

[1m 94/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - loss: 0.0976

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textbf{ 95/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 43ms/step - loss: 0.0975: end{sphinxVerbatim}

[1m 95/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - loss: 0.0975

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textbf{ 96/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0975: end{sphinxVerbatim}

[1m 96/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - loss: 0.0975

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textbf{ 97/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0974: end{sphinxVerbatim}

[1m 97/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - loss: 0.0974

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0974: end{sphinxVerbatim}

[1m 98/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - loss: 0.0974

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textbf{ 99/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0973: end{sphinxVerbatim}

[1m 99/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - loss: 0.0973

100/422 ━━━━━━━━━━━━━━━━━━━━ 14s 45ms/step - loss: 0.0973: </pre>
textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0973: end{sphinxVerbatim}

[1m100/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0973

101/422 ━━━━━━━━━━━━━━━━━━━━ 14s 45ms/step - loss: 0.0973: </pre>
textbf{101/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0973: end{sphinxVerbatim}

[1m101/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0973

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0972: end{sphinxVerbatim}

[1m102/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0972

103/422 ━━━━━━━━━━━━━━━━━━━━ 14s 45ms/step - loss: 0.0972: </pre>
textbf{103/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0972: end{sphinxVerbatim}

[1m103/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0972

104/422 ━━━━━━━━━━━━━━━━━━━━ 14s 45ms/step - loss: 0.0972: </pre>
textbf{104/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0972: end{sphinxVerbatim}

[1m104/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0972

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textbf{105/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0971: end{sphinxVerbatim}

[1m105/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0971

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textbf{106/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0971: end{sphinxVerbatim}

[1m106/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0971

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textbf{107/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 45ms/step - loss: 0.0971: end{sphinxVerbatim}

[1m107/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - loss: 0.0971

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textbf{108/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0970: end{sphinxVerbatim}

[1m108/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0970

109/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - loss: 0.0970: </pre>
textbf{109/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0970: end{sphinxVerbatim}

[1m109/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0970

110/422 ━━━━━━━━━━━━━━━━━━━━ 14s 46ms/step - loss: 0.0970: </pre>
textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0970: end{sphinxVerbatim}

[1m110/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0970

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0969: end{sphinxVerbatim}

[1m112/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0969

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textbf{113/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0968: end{sphinxVerbatim}

[1m113/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0968

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textbf{114/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0968: end{sphinxVerbatim}

[1m114/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0968

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textbf{115/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0967: end{sphinxVerbatim}

[1m115/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0967

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textbf{116/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0967: end{sphinxVerbatim}

[1m116/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0967

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textbf{117/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0967: end{sphinxVerbatim}

[1m117/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0967

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textbf{118/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 46ms/step - loss: 0.0966: end{sphinxVerbatim}

[1m118/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - loss: 0.0966

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textbf{119/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 47ms/step - loss: 0.0966: end{sphinxVerbatim}

[1m119/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - loss: 0.0966

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 47ms/step - loss: 0.0964: end{sphinxVerbatim}

[1m121/422[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - loss: 0.0964

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textbf{122/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 47ms/step - loss: 0.0964: end{sphinxVerbatim}

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textbf{123/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 47ms/step - loss: 0.0963: end{sphinxVerbatim}

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textbf{125/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{13s} 47ms/step - loss: 0.0962: end{sphinxVerbatim}

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textbf{126/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{13s} 47ms/step - loss: 0.0962: end{sphinxVerbatim}

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textbf{127/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 47ms/step - loss: 0.0961: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 47ms/step - loss: 0.0961: end{sphinxVerbatim}

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textbf{129/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 47ms/step - loss: 0.0961: end{sphinxVerbatim}

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textbf{130/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0960: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0960: end{sphinxVerbatim}

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textbf{132/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0959: end{sphinxVerbatim}

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textbf{133/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0959: end{sphinxVerbatim}

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textbf{134/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0959: end{sphinxVerbatim}

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textbf{135/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0958: end{sphinxVerbatim}

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textbf{137/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0958: end{sphinxVerbatim}

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textbf{139/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0957: end{sphinxVerbatim}

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textbf{141/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0956: end{sphinxVerbatim}

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textbf{143/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0956: end{sphinxVerbatim}

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textbf{145/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0955: end{sphinxVerbatim}

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textbf{147/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0954: end{sphinxVerbatim}

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textbf{148/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 48ms/step - loss: 0.0954: end{sphinxVerbatim}

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textbf{149/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 49ms/step - loss: 0.0953: end{sphinxVerbatim}

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textbf{150/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 49ms/step - loss: 0.0953: end{sphinxVerbatim}

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textbf{151/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 49ms/step - loss: 0.0952: end{sphinxVerbatim}

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textbf{152/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 49ms/step - loss: 0.0952: end{sphinxVerbatim}

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153/422 ━━━━━━━━━━━━━━━━━━━━ 13s 49ms/step - loss: 0.0952: </pre>
textbf{153/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 49ms/step - loss: 0.0952: end{sphinxVerbatim}

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textbf{154/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0951: end{sphinxVerbatim}

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textbf{155/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0951: end{sphinxVerbatim}

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textbf{156/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0950: end{sphinxVerbatim}

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textbf{157/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0950: end{sphinxVerbatim}

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textbf{158/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0949: end{sphinxVerbatim}

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textbf{159/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0949: end{sphinxVerbatim}

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textbf{161/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0948: end{sphinxVerbatim}

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textbf{162/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0947: end{sphinxVerbatim}

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textbf{163/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0947: end{sphinxVerbatim}

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textbf{164/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0947: end{sphinxVerbatim}

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textbf{165/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0946: end{sphinxVerbatim}

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textbf{166/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0946: end{sphinxVerbatim}

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textbf{167/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0945: end{sphinxVerbatim}

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textbf{168/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{12s} 50ms/step - loss: 0.0945: end{sphinxVerbatim}

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textbf{227/422} textcolor{ansi-green}{━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━} textbf{9s} 51ms/step - loss: 0.0925: end{sphinxVerbatim}

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textbf{229/422} textcolor{ansi-green}{━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━} textbf{9s} 51ms/step - loss: 0.0924: end{sphinxVerbatim}

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textbf{231/422} textcolor{ansi-green}{━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━} textbf{9s} 50ms/step - loss: 0.0924: end{sphinxVerbatim}

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textbf{235/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{9s} 50ms/step - loss: 0.0923: end{sphinxVerbatim}

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textbf{237/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{9s} 50ms/step - loss: 0.0922: end{sphinxVerbatim}

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textbf{245/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0920: end{sphinxVerbatim}

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textbf{247/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0919: end{sphinxVerbatim}

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textbf{249/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0919: end{sphinxVerbatim}

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textbf{251/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0918: end{sphinxVerbatim}

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textbf{253/422} textcolor{ansi-green}{━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0918: end{sphinxVerbatim}

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textbf{255/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{8s} 49ms/step - loss: 0.0917: end{sphinxVerbatim}

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textbf{257/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{7s} 48ms/step - loss: 0.0917: end{sphinxVerbatim}

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textbf{259/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{7s} 48ms/step - loss: 0.0916: end{sphinxVerbatim}

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textbf{261/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{7s} 48ms/step - loss: 0.0916: end{sphinxVerbatim}

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textbf{265/422} textcolor{ansi-green}{━━━━━━━━━━━━}textcolor{ansi-white}{━━━━━━━━} textbf{7s} 48ms/step - loss: 0.0915: end{sphinxVerbatim}

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textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0887: end{sphinxVerbatim}

[1m388/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0887

389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0887: </pre>
textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0887: end{sphinxVerbatim}

[1m389/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0887

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0886: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0886: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0886

392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0886: </pre>
textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0886: end{sphinxVerbatim}

[1m392/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0886

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0886: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0886: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0886

394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0886: </pre>
textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0886: end{sphinxVerbatim}

[1m394/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0886

395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0885: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0885: end{sphinxVerbatim}

[1m395/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0885

396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0885: </pre>
textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0885: end{sphinxVerbatim}

[1m396/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0885

397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0885: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0885: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0885

399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0885: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0885: end{sphinxVerbatim}

[1m399/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0885

400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0885: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 51ms/step - loss: 0.0885: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 51ms/step - loss: 0.0885

401/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0884: </pre>
textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 51ms/step - loss: 0.0884: end{sphinxVerbatim}

[1m401/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 51ms/step - loss: 0.0884

402/422 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - loss: 0.0884: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 51ms/step - loss: 0.0884: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 51ms/step - loss: 0.0884

403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0884: </pre>
textbf{403/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0884: end{sphinxVerbatim}

[1m403/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0884

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0884: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0884: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0884

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0884: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0884: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0884

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0883: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0883: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0883

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0883: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0883: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0883

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0882: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0882: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0882

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0882: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0882: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0882

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0882: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0882: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0882

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0881: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0881: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0881

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0881: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 51ms/step - loss: 0.0881: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 51ms/step - loss: 0.0881

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - loss: 0.0881: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 51ms/step - loss: 0.0881: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 51ms/step - loss: 0.0881

422/422 ━━━━━━━━━━━━━━━━━━━━ 39s 52ms/step - loss: 0.0881 - val_loss: 0.0469: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{39s} 52ms/step - loss: 0.0881 - val_loss: 0.0469: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m39s[0m 52ms/step - loss: 0.0881 - val_loss: 0.0469

Epoch 4/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 2:14:47 19s/step - loss: 0.1190: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{2:14:47} 19s/step - loss: 0.1190: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:14:47[0m 19s/step - loss: 0.1190

 2/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - loss: 0.1045: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 60ms/step - loss: 0.1045: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 60ms/step - loss: 0.1045

 3/422 ━━━━━━━━━━━━━━━━━━━━ 24s 59ms/step - loss: 0.1013: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 59ms/step - loss: 0.1013: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 59ms/step - loss: 0.1013

 4/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - loss: 0.0981: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 60ms/step - loss: 0.0981: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 60ms/step - loss: 0.0981

 5/422 ━━━━━━━━━━━━━━━━━━━━ 24s 58ms/step - loss: 0.0992: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 58ms/step - loss: 0.0992: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 58ms/step - loss: 0.0992

 6/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0980: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0980: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0980

 7/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.0963: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0963: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.0963

 8/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0944: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0944: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0944

 9/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0926: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0926: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0926

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textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0912: end{sphinxVerbatim}

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textbf{ 70/422} textcolor{ansi-green}{━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━} textbf{20s} 59ms/step - loss: 0.0812: end{sphinxVerbatim}

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textbf{ 87/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{18s} 56ms/step - loss: 0.0802: end{sphinxVerbatim}

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textbf{ 89/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{18s} 56ms/step - loss: 0.0802: end{sphinxVerbatim}

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textbf{ 91/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{18s} 55ms/step - loss: 0.0801: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 55ms/step - loss: 0.0797: end{sphinxVerbatim}

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 56ms/step - loss: 0.0790: end{sphinxVerbatim}

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textbf{130/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 56ms/step - loss: 0.0786: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{16s} 56ms/step - loss: 0.0786: end{sphinxVerbatim}

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textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 59ms/step - loss: 0.0733: end{sphinxVerbatim}

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textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 59ms/step - loss: 0.0733: end{sphinxVerbatim}

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386/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - loss: 0.0732: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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387/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - loss: 0.0732: </pre>
textbf{387/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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388/422 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - loss: 0.0732: </pre>
textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{2s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0732: </pre>
textbf{390/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0732: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0732: </pre>
textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0732: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0732: end{sphinxVerbatim}

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textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0731: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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textbf{398/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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399/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0731: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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400/422 ━━━━━━━━━━━━━━━━━━━━ 1s 59ms/step - loss: 0.0731: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 59ms/step - loss: 0.0731: end{sphinxVerbatim}

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textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{1s} 59ms/step - loss: 0.0730: end{sphinxVerbatim}

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textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0730: end{sphinxVerbatim}

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textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0730: end{sphinxVerbatim}

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410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0730: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0730

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0730: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0730

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0729: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0729

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0729: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0729

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 60ms/step - loss: 0.0729: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 60ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 60ms/step - loss: 0.0729

422/422 ━━━━━━━━━━━━━━━━━━━━ 45s 62ms/step - loss: 0.0728 - val_loss: 0.0424: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{45s} 62ms/step - loss: 0.0728 - val_loss: 0.0424: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m45s[0m 62ms/step - loss: 0.0728 - val_loss: 0.0424

Epoch 5/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 1:44:34 15s/step - loss: 0.0792: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{1:44:34} 15s/step - loss: 0.0792: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:44:34[0m 15s/step - loss: 0.0792

 2/422 ━━━━━━━━━━━━━━━━━━━━ 33s 81ms/step - loss: 0.0786: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{33s} 81ms/step - loss: 0.0786: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 81ms/step - loss: 0.0786

 3/422 ━━━━━━━━━━━━━━━━━━━━ 32s 78ms/step - loss: 0.0802: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{32s} 78ms/step - loss: 0.0802: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 78ms/step - loss: 0.0802

 5/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - loss: 0.0809: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 63ms/step - loss: 0.0809: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 63ms/step - loss: 0.0809

 6/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0805: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0805: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0805

 7/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0797: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0797: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0797

 8/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0787: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0787: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0787

 9/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0775: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0775: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0775

 10/422 ━━━━━━━━━━━━━━━━━━━━ 24s 61ms/step - loss: 0.0766: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 61ms/step - loss: 0.0766: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - loss: 0.0766

 11/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0755: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0755: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0755

 12/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.0747: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0747: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.0747

 13/422 ━━━━━━━━━━━━━━━━━━━━ 25s 63ms/step - loss: 0.0738: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 63ms/step - loss: 0.0738: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 63ms/step - loss: 0.0738

 14/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.0729: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.0729

 15/422 ━━━━━━━━━━━━━━━━━━━━ 25s 63ms/step - loss: 0.0721: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 63ms/step - loss: 0.0721: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 63ms/step - loss: 0.0721

 16/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.0715: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0715: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.0715

 18/422 ━━━━━━━━━━━━━━━━━━━━ 24s 61ms/step - loss: 0.0709: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 61ms/step - loss: 0.0709: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - loss: 0.0709

 19/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0706: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0706: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0706

 20/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0705: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0705: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0705

 21/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0704: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0704

 23/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0704: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0704

 25/422 ━━━━━━━━━━━━━━━━━━━━ 22s 56ms/step - loss: 0.0702: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 56ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 56ms/step - loss: 0.0702

 27/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0702: </pre>
textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0702: end{sphinxVerbatim}

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textbf{ 83/422} textcolor{ansi-green}{━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━} textbf{16s} 47ms/step - loss: 0.0696: end{sphinxVerbatim}

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textbf{ 84/422} textcolor{ansi-green}{━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━} textbf{16s} 48ms/step - loss: 0.0696: end{sphinxVerbatim}

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textbf{ 85/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 48ms/step - loss: 0.0695: end{sphinxVerbatim}

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textbf{ 86/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0694: end{sphinxVerbatim}

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textbf{ 88/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0694: end{sphinxVerbatim}

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textbf{ 89/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0693: end{sphinxVerbatim}

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textbf{ 90/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0693: end{sphinxVerbatim}

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textbf{ 91/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0692: end{sphinxVerbatim}

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textbf{ 92/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0692: end{sphinxVerbatim}

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textbf{ 93/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 49ms/step - loss: 0.0691: end{sphinxVerbatim}

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textbf{ 94/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 50ms/step - loss: 0.0691: end{sphinxVerbatim}

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textbf{ 95/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 50ms/step - loss: 0.0690: end{sphinxVerbatim}

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textbf{ 96/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 50ms/step - loss: 0.0690: end{sphinxVerbatim}

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textbf{ 97/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 50ms/step - loss: 0.0689: end{sphinxVerbatim}

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 50ms/step - loss: 0.0689: end{sphinxVerbatim}

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textbf{ 99/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0689: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0688: end{sphinxVerbatim}

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textbf{101/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0688: end{sphinxVerbatim}

[1m101/422[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - loss: 0.0688

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0687: end{sphinxVerbatim}

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textbf{103/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0687: end{sphinxVerbatim}

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textbf{104/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0687: end{sphinxVerbatim}

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textbf{105/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{106/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{107/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{109/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0685: end{sphinxVerbatim}

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textbf{111/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0685: end{sphinxVerbatim}

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0685: end{sphinxVerbatim}

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textbf{113/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{114/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{115/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{116/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{117/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0683: end{sphinxVerbatim}

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textbf{118/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0683: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{16s} 53ms/step - loss: 0.0683: end{sphinxVerbatim}

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textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 54ms/step - loss: 0.0646

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 54ms/step - loss: 0.0646: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 54ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 54ms/step - loss: 0.0646

422/422 ━━━━━━━━━━━━━━━━━━━━ 38s 56ms/step - loss: 0.0646 - val_loss: 0.0371: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{38s} 56ms/step - loss: 0.0646 - val_loss: 0.0371: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m38s[0m 56ms/step - loss: 0.0646 - val_loss: 0.0371

Epoch 6/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 65ms/step - loss: 0.0920: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 65ms/step - loss: 0.0920: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 65ms/step - loss: 0.0920

 3/422 ━━━━━━━━━━━━━━━━━━━━ 14s 35ms/step - loss: 0.0708: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{14s} 35ms/step - loss: 0.0708: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 35ms/step - loss: 0.0708

 5/422 ━━━━━━━━━━━━━━━━━━━━ 16s 38ms/step - loss: 0.0656: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 38ms/step - loss: 0.0656: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 38ms/step - loss: 0.0656

 7/422 ━━━━━━━━━━━━━━━━━━━━ 14s 35ms/step - loss: 0.0629: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{14s} 35ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 35ms/step - loss: 0.0629

 9/422 ━━━━━━━━━━━━━━━━━━━━ 13s 33ms/step - loss: 0.0598: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{13s} 33ms/step - loss: 0.0598: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 33ms/step - loss: 0.0598

 11/422 ━━━━━━━━━━━━━━━━━━━━ 13s 33ms/step - loss: 0.0568: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{13s} 33ms/step - loss: 0.0568: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 33ms/step - loss: 0.0568

 13/422 ━━━━━━━━━━━━━━━━━━━━ 13s 32ms/step - loss: 0.0548: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{13s} 32ms/step - loss: 0.0548: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 32ms/step - loss: 0.0548

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textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 32ms/step - loss: 0.0535: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 32ms/step - loss: 0.0535

 17/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0533: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0533

 19/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0534: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0534

 21/422 ━━━━━━━━━━━━━━━━━━━━ 12s 32ms/step - loss: 0.0540: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 32ms/step - loss: 0.0540: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 32ms/step - loss: 0.0540

 23/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0545: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0545: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0545

 25/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0548: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0548: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0548

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textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0553: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0553

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textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0558: end{sphinxVerbatim}

[1m 29/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0558

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textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0561: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0561

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0564: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0564

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textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0567: end{sphinxVerbatim}

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{11s} 31ms/step - loss: 0.0569: end{sphinxVerbatim}

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{11s} 31ms/step - loss: 0.0573: end{sphinxVerbatim}

[1m 39/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 31ms/step - loss: 0.0573

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textbf{ 41/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 32ms/step - loss: 0.0577: end{sphinxVerbatim}

[1m 41/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 32ms/step - loss: 0.0577

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textbf{ 42/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{12s} 33ms/step - loss: 0.0578: end{sphinxVerbatim}

[1m 42/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 33ms/step - loss: 0.0578

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textbf{ 43/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{12s} 33ms/step - loss: 0.0580: end{sphinxVerbatim}

[1m 43/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 33ms/step - loss: 0.0580

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textbf{ 44/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{12s} 34ms/step - loss: 0.0581: end{sphinxVerbatim}

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textbf{ 45/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{12s} 34ms/step - loss: 0.0583: end{sphinxVerbatim}

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textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0588: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0588

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0587: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0587: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0587

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step - loss: 0.0587: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 59ms/step - loss: 0.0587: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 59ms/step - loss: 0.0587

422/422 ━━━━━━━━━━━━━━━━━━━━ 26s 61ms/step - loss: 0.0587 - val_loss: 0.0357: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 61ms/step - loss: 0.0587 - val_loss: 0.0357: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 61ms/step - loss: 0.0587 - val_loss: 0.0357

Epoch 7/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 1:46:31 15s/step - loss: 0.0952: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{1:46:31} 15s/step - loss: 0.0952: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:46:31[0m 15s/step - loss: 0.0952

 2/422 ━━━━━━━━━━━━━━━━━━━━ 29s 71ms/step - loss: 0.0804: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{29s} 71ms/step - loss: 0.0804: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 71ms/step - loss: 0.0804

 3/422 ━━━━━━━━━━━━━━━━━━━━ 28s 69ms/step - loss: 0.0782: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{28s} 69ms/step - loss: 0.0782: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 69ms/step - loss: 0.0782

 4/422 ━━━━━━━━━━━━━━━━━━━━ 27s 67ms/step - loss: 0.0748: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 67ms/step - loss: 0.0748: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 67ms/step - loss: 0.0748

 5/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - loss: 0.0759: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 64ms/step - loss: 0.0759: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 64ms/step - loss: 0.0759

 6/422 ━━━━━━━━━━━━━━━━━━━━ 26s 64ms/step - loss: 0.0754: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 64ms/step - loss: 0.0754: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 64ms/step - loss: 0.0754

 8/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - loss: 0.0728: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 60ms/step - loss: 0.0728: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 60ms/step - loss: 0.0728

 9/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0712: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0712: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0712

 10/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0697: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0697: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0697

 11/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0681: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0681: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0681

 12/422 ━━━━━━━━━━━━━━━━━━━━ 24s 61ms/step - loss: 0.0667: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 61ms/step - loss: 0.0667: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - loss: 0.0667

 13/422 ━━━━━━━━━━━━━━━━━━━━ 24s 61ms/step - loss: 0.0655: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 61ms/step - loss: 0.0655: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - loss: 0.0655

 14/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0644: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0644: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0644

 16/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0629: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0629

 17/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0625: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0625: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0625

 18/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0623: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0623: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0623

 19/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0621: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0621: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0621

 20/422 ━━━━━━━━━━━━━━━━━━━━ 23s 59ms/step - loss: 0.0620: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 59ms/step - loss: 0.0620: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 59ms/step - loss: 0.0620

 21/422 ━━━━━━━━━━━━━━━━━━━━ 23s 59ms/step - loss: 0.0619: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 59ms/step - loss: 0.0619: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 59ms/step - loss: 0.0619

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textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 59ms/step - loss: 0.0618: end{sphinxVerbatim}

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textbf{ 90/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0593: end{sphinxVerbatim}

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textbf{ 92/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0592: end{sphinxVerbatim}

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textbf{ 94/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0591: end{sphinxVerbatim}

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textbf{ 96/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0591: end{sphinxVerbatim}

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 37ms/step - loss: 0.0590: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 37ms/step - loss: 0.0590: end{sphinxVerbatim}

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0589: end{sphinxVerbatim}

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textbf{104/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0589: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0588: end{sphinxVerbatim}

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

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textbf{114/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0587: end{sphinxVerbatim}

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textbf{116/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0587: end{sphinxVerbatim}

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textbf{118/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0586: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0586: end{sphinxVerbatim}

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textbf{124/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0585: end{sphinxVerbatim}

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textbf{126/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0584: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 35ms/step - loss: 0.0584: end{sphinxVerbatim}

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textbf{130/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 35ms/step - loss: 0.0584: end{sphinxVerbatim}

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textbf{134/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 35ms/step - loss: 0.0583: end{sphinxVerbatim}

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textbf{136/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 35ms/step - loss: 0.0583: end{sphinxVerbatim}

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textbf{138/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0583: end{sphinxVerbatim}

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textbf{140/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0582: end{sphinxVerbatim}

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textbf{142/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0582: end{sphinxVerbatim}

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textbf{144/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0582: end{sphinxVerbatim}

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textbf{146/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0581: end{sphinxVerbatim}

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textbf{150/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0581: end{sphinxVerbatim}

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textbf{152/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{9s} 35ms/step - loss: 0.0581: end{sphinxVerbatim}

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textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 45ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 45ms/step - loss: 0.0554

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - loss: 0.0554: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 46ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 46ms/step - loss: 0.0554

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - loss: 0.0554: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 46ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 46ms/step - loss: 0.0554

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 46ms/step - loss: 0.0554: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 46ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 46ms/step - loss: 0.0554

422/422 ━━━━━━━━━━━━━━━━━━━━ 35s 48ms/step - loss: 0.0554 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{35s} 48ms/step - loss: 0.0554 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m35s[0m 48ms/step - loss: 0.0554 - val_loss: 0.0342

Epoch 8/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 2:45 394ms/step - loss: 0.0872: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{2:45} 394ms/step - loss: 0.0872: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:45[0m 394ms/step - loss: 0.0872

 2/422 ━━━━━━━━━━━━━━━━━━━━ 28s 68ms/step - loss: 0.0809: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{28s} 68ms/step - loss: 0.0809: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 68ms/step - loss: 0.0809

 3/422 ━━━━━━━━━━━━━━━━━━━━ 27s 66ms/step - loss: 0.0774: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 66ms/step - loss: 0.0774: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 66ms/step - loss: 0.0774

 4/422 ━━━━━━━━━━━━━━━━━━━━ 27s 65ms/step - loss: 0.0733: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 65ms/step - loss: 0.0733: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 65ms/step - loss: 0.0733

 5/422 ━━━━━━━━━━━━━━━━━━━━ 27s 65ms/step - loss: 0.0760: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 65ms/step - loss: 0.0760: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 65ms/step - loss: 0.0760

 7/422 ━━━━━━━━━━━━━━━━━━━━ 25s 60ms/step - loss: 0.0750: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 60ms/step - loss: 0.0750: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 60ms/step - loss: 0.0750

 8/422 ━━━━━━━━━━━━━━━━━━━━ 24s 59ms/step - loss: 0.0737: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 59ms/step - loss: 0.0737: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 59ms/step - loss: 0.0737

 9/422 ━━━━━━━━━━━━━━━━━━━━ 24s 59ms/step - loss: 0.0725: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 59ms/step - loss: 0.0725: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 59ms/step - loss: 0.0725

 10/422 ━━━━━━━━━━━━━━━━━━━━ 24s 60ms/step - loss: 0.0711: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 60ms/step - loss: 0.0711: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 60ms/step - loss: 0.0711

 11/422 ━━━━━━━━━━━━━━━━━━━━ 24s 61ms/step - loss: 0.0697: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24s} 61ms/step - loss: 0.0697: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 61ms/step - loss: 0.0697

 13/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0672: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0672: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0672

 14/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0660: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0660: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0660

 16/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0644: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0644: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0644

 17/422 ━━━━━━━━━━━━━━━━━━━━ 23s 57ms/step - loss: 0.0640: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 57ms/step - loss: 0.0640: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 57ms/step - loss: 0.0640

 18/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0637: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0637: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0637

 19/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0634: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0634: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0634

 20/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0632: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0632: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0632

 21/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0629: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0629

 23/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0623: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0623: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0623

 24/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0620: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0620: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0620

 25/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0616: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0616: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0616

 26/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0615: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0615: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0615

 27/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0613: </pre>
textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0613: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0613

 28/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0612: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0612: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0612

 29/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0611: </pre>
textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0611: end{sphinxVerbatim}

[1m 29/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0611

 30/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0609: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0609: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0609

 31/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0608: </pre>
textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0608: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0608

 32/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0606: </pre>
textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0606: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0606

 33/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0604: </pre>
textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0604: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0604

 34/422 ━━━━━━━━━━━━━━━━━━━━ 22s 58ms/step - loss: 0.0604: </pre>
textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0604: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0604

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textbf{ 96/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0562: end{sphinxVerbatim}

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0562: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{14s} 44ms/step - loss: 0.0561: end{sphinxVerbatim}

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{13s} 43ms/step - loss: 0.0561: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{13s} 43ms/step - loss: 0.0559: end{sphinxVerbatim}

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textbf{118/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{12s} 42ms/step - loss: 0.0557: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{12s} 41ms/step - loss: 0.0556: end{sphinxVerbatim}

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textbf{124/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{12s} 41ms/step - loss: 0.0555: end{sphinxVerbatim}

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textbf{126/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{12s} 41ms/step - loss: 0.0555: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 41ms/step - loss: 0.0554: end{sphinxVerbatim}

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textbf{130/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0554: end{sphinxVerbatim}

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textbf{132/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0554: end{sphinxVerbatim}

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textbf{134/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0553: end{sphinxVerbatim}

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textbf{136/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0553: end{sphinxVerbatim}

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textbf{140/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0552: end{sphinxVerbatim}

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textbf{142/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 40ms/step - loss: 0.0552: end{sphinxVerbatim}

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textbf{144/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 39ms/step - loss: 0.0552: end{sphinxVerbatim}

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textbf{146/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 39ms/step - loss: 0.0551: end{sphinxVerbatim}

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textbf{150/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 39ms/step - loss: 0.0551: end{sphinxVerbatim}

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textbf{152/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 39ms/step - loss: 0.0550: end{sphinxVerbatim}

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textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 45ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 45ms/step - loss: 0.0524

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - loss: 0.0524: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 45ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 45ms/step - loss: 0.0524

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - loss: 0.0524: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 45ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 45ms/step - loss: 0.0524

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step - loss: 0.0524: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 45ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 45ms/step - loss: 0.0524

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 47ms/step - loss: 0.0524 - val_loss: 0.0331: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 47ms/step - loss: 0.0524 - val_loss: 0.0331: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 47ms/step - loss: 0.0524 - val_loss: 0.0331

Epoch 9/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 3:38 519ms/step - loss: 0.0569: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{3:38} 519ms/step - loss: 0.0569: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:38[0m 519ms/step - loss: 0.0569

 2/422 ━━━━━━━━━━━━━━━━━━━━ 25s 61ms/step - loss: 0.0657: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 61ms/step - loss: 0.0657: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 61ms/step - loss: 0.0657

 3/422 ━━━━━━━━━━━━━━━━━━━━ 25s 62ms/step - loss: 0.0630: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 62ms/step - loss: 0.0630: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 62ms/step - loss: 0.0630

 5/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0619: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0619: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0619

 6/422 ━━━━━━━━━━━━━━━━━━━━ 23s 57ms/step - loss: 0.0611: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 57ms/step - loss: 0.0611: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 57ms/step - loss: 0.0611

 7/422 ━━━━━━━━━━━━━━━━━━━━ 23s 57ms/step - loss: 0.0602: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 57ms/step - loss: 0.0602: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 57ms/step - loss: 0.0602

 8/422 ━━━━━━━━━━━━━━━━━━━━ 23s 56ms/step - loss: 0.0590: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23s} 56ms/step - loss: 0.0590: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 56ms/step - loss: 0.0590

 9/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0579: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0579: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0579

 10/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0571: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0571: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0571

 12/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0556: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0556

 13/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0550: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0550: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0550

 14/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0543: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0543: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0543

 15/422 ━━━━━━━━━━━━━━━━━━━━ 22s 56ms/step - loss: 0.0539: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 56ms/step - loss: 0.0539: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 56ms/step - loss: 0.0539

 17/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0534: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0534

 18/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0534: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0534

 19/422 ━━━━━━━━━━━━━━━━━━━━ 22s 55ms/step - loss: 0.0534: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 55ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 55ms/step - loss: 0.0534

 20/422 ━━━━━━━━━━━━━━━━━━━━ 22s 56ms/step - loss: 0.0534: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 56ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 56ms/step - loss: 0.0534

 21/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0534: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0534

 22/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0533: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0533

 23/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0533: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0533

 24/422 ━━━━━━━━━━━━━━━━━━━━ 23s 58ms/step - loss: 0.0532: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{23s} 58ms/step - loss: 0.0532: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 58ms/step - loss: 0.0532

 26/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0530: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0530

 28/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0529: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0529

 30/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0530: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0530

 32/422 ━━━━━━━━━━━━━━━━━━━━ 21s 56ms/step - loss: 0.0529: </pre>
textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 56ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 56ms/step - loss: 0.0529

 33/422 ━━━━━━━━━━━━━━━━━━━━ 21s 56ms/step - loss: 0.0529: </pre>
textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 56ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 56ms/step - loss: 0.0529

 34/422 ━━━━━━━━━━━━━━━━━━━━ 21s 57ms/step - loss: 0.0530: </pre>
textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 57ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 57ms/step - loss: 0.0530

 35/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0530: </pre>
textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 35/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0530

 36/422 ━━━━━━━━━━━━━━━━━━━━ 22s 57ms/step - loss: 0.0530: </pre>
textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 57ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 36/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 57ms/step - loss: 0.0530

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{22s} 58ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 37/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 58ms/step - loss: 0.0530

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textbf{ 87/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 53ms/step - loss: 0.0524: end{sphinxVerbatim}

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textbf{ 89/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 53ms/step - loss: 0.0523: end{sphinxVerbatim}

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textbf{ 91/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 53ms/step - loss: 0.0523: end{sphinxVerbatim}

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textbf{ 93/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 53ms/step - loss: 0.0522: end{sphinxVerbatim}

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textbf{ 95/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{17s} 52ms/step - loss: 0.0521: end{sphinxVerbatim}

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textbf{ 97/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0521: end{sphinxVerbatim}

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textbf{ 99/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 52ms/step - loss: 0.0521: end{sphinxVerbatim}

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textbf{101/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{16s} 51ms/step - loss: 0.0520: end{sphinxVerbatim}

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textbf{109/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{15s} 50ms/step - loss: 0.0519: end{sphinxVerbatim}

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textbf{111/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{15s} 50ms/step - loss: 0.0519: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0518: end{sphinxVerbatim}

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textbf{122/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0518: end{sphinxVerbatim}

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textbf{124/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{126/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{129/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{132/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{133/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{134/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{135/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 49ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{136/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 50ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{137/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 50ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{138/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{14s} 50ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{142/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{13s} 50ms/step - loss: 0.0515: end{sphinxVerbatim}

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textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 56ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 56ms/step - loss: 0.0491

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 56ms/step - loss: 0.0491: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 56ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 56ms/step - loss: 0.0491

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 56ms/step - loss: 0.0491: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 56ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 56ms/step - loss: 0.0491

422/422 ━━━━━━━━━━━━━━━━━━━━ 25s 57ms/step - loss: 0.0491 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{25s} 57ms/step - loss: 0.0491 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m25s[0m 57ms/step - loss: 0.0491 - val_loss: 0.0342

Epoch 10/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 63ms/step - loss: 0.0320: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 63ms/step - loss: 0.0320: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 63ms/step - loss: 0.0320

 3/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0375: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0375: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0375

 5/422 ━━━━━━━━━━━━━━━━━━━━ 12s 31ms/step - loss: 0.0399: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{12s} 31ms/step - loss: 0.0399: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 31ms/step - loss: 0.0399

 7/422 ━━━━━━━━━━━━━━━━━━━━ 14s 34ms/step - loss: 0.0404: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{14s} 34ms/step - loss: 0.0404: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 34ms/step - loss: 0.0404

 9/422 ━━━━━━━━━━━━━━━━━━━━ 15s 38ms/step - loss: 0.0401: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15s} 38ms/step - loss: 0.0401: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 38ms/step - loss: 0.0401

 10/422 ━━━━━━━━━━━━━━━━━━━━ 16s 39ms/step - loss: 0.0399: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 39ms/step - loss: 0.0399: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 39ms/step - loss: 0.0399

 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0396: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0396: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0396

 12/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0392: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0392: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0392

 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0388: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0388: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0388

 14/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - loss: 0.0385: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 46ms/step - loss: 0.0385: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 46ms/step - loss: 0.0385

 15/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - loss: 0.0383: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 47ms/step - loss: 0.0383: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 47ms/step - loss: 0.0383

 16/422 ━━━━━━━━━━━━━━━━━━━━ 19s 49ms/step - loss: 0.0384: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 49ms/step - loss: 0.0384: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 49ms/step - loss: 0.0384

 17/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - loss: 0.0388: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 50ms/step - loss: 0.0388: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 50ms/step - loss: 0.0388

 18/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - loss: 0.0393: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 50ms/step - loss: 0.0393: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 50ms/step - loss: 0.0393

 19/422 ━━━━━━━━━━━━━━━━━━━━ 20s 52ms/step - loss: 0.0397: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 52ms/step - loss: 0.0397: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 52ms/step - loss: 0.0397

 20/422 ━━━━━━━━━━━━━━━━━━━━ 21s 52ms/step - loss: 0.0401: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 52ms/step - loss: 0.0401: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 52ms/step - loss: 0.0401

 21/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - loss: 0.0404: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 53ms/step - loss: 0.0404: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 53ms/step - loss: 0.0404

 22/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - loss: 0.0407: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 53ms/step - loss: 0.0407: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 53ms/step - loss: 0.0407

 23/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - loss: 0.0410: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 53ms/step - loss: 0.0410: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 53ms/step - loss: 0.0410

 24/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - loss: 0.0412: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 53ms/step - loss: 0.0412: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 53ms/step - loss: 0.0412

 25/422 ━━━━━━━━━━━━━━━━━━━━ 21s 53ms/step - loss: 0.0414: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 53ms/step - loss: 0.0414: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 53ms/step - loss: 0.0414

 26/422 ━━━━━━━━━━━━━━━━━━━━ 21s 54ms/step - loss: 0.0417: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 54ms/step - loss: 0.0417: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 54ms/step - loss: 0.0417

 27/422 ━━━━━━━━━━━━━━━━━━━━ 21s 54ms/step - loss: 0.0419: </pre>
textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 54ms/step - loss: 0.0419: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 54ms/step - loss: 0.0419

 28/422 ━━━━━━━━━━━━━━━━━━━━ 21s 54ms/step - loss: 0.0421: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 54ms/step - loss: 0.0421: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 54ms/step - loss: 0.0421

 29/422 ━━━━━━━━━━━━━━━━━━━━ 21s 54ms/step - loss: 0.0424: </pre>
textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 54ms/step - loss: 0.0424: end{sphinxVerbatim}

[1m 29/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 54ms/step - loss: 0.0424

 30/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0427: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0427: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0427

 31/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0429: </pre>
textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0429: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0429

 32/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0431: </pre>
textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0431: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0431

 33/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0433: </pre>
textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0433: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0433

 34/422 ━━━━━━━━━━━━━━━━━━━━ 21s 55ms/step - loss: 0.0435: </pre>
textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0435: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0435

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textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{21s} 55ms/step - loss: 0.0437: end{sphinxVerbatim}

[1m 35/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 55ms/step - loss: 0.0437

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[33]:

MultiDimensionalClassifier(
    model=<function get_model at 0x7f3bf87d5120>
    build_fn=None
    warm_start=False
    random_state=0
    optimizer=rmsprop
    loss=None
    metrics=None
    batch_size=128
    validation_batch_size=None
    verbose=1
    callbacks=None
    validation_split=0.1
    shuffle=True
    run_eagerly=False
    epochs=10
    class_weight=None
)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

[34]:

score = clf.score(x_test, y_test)
print(f"Test score (accuracy): {score:.2f}")

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Test score (accuracy): 0.99