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, :])

[[0.4701164]
 [0.5412356]]

[11]:

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

[[0.18440247 0.19759478 0.19069979 0.2605673  0.16673572]
 [0.15900476 0.20451763 0.18233575 0.26660454 0.18753728]]

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, 1, 0, 1]), array([2, 2, 4, 0])]

[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([[0, 3],
       [1, 3],
       [1, 1],
       [1, 0],
       [0, 3]])

[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]:

0.425

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]:

-4.123792769543099

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]:

-2.744013615915596

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{364/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 38ms/step - loss: 0.7272: end{sphinxVerbatim}

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366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step - loss: 0.7253: </pre>
textbf{366/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 38ms/step - loss: 0.7253: end{sphinxVerbatim}

[1m366/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 38ms/step - loss: 0.7253

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textbf{368/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 38ms/step - loss: 0.7233: end{sphinxVerbatim}

[1m368/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 38ms/step - loss: 0.7233

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textbf{370/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.7213: end{sphinxVerbatim}

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372/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7194: </pre>
textbf{372/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.7194: end{sphinxVerbatim}

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376/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7155: </pre>
textbf{376/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.7155: end{sphinxVerbatim}

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378/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7136: </pre>
textbf{378/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.7136: end{sphinxVerbatim}

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379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7127: </pre>
textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.7127: end{sphinxVerbatim}

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381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7108: </pre>
textbf{381/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7108: end{sphinxVerbatim}

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383/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7089: </pre>
textbf{383/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7089: end{sphinxVerbatim}

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385/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7071: </pre>
textbf{385/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7071: end{sphinxVerbatim}

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386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7062: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7062: end{sphinxVerbatim}

[1m386/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.7062

387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7052: </pre>
textbf{387/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7052: end{sphinxVerbatim}

[1m387/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.7052

389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7034: </pre>
textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7034: end{sphinxVerbatim}

[1m389/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.7034

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7016: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7016: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.7016

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.6998: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.6998: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.6998

395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.6980: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.6980: end{sphinxVerbatim}

[1m395/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.6980

397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6962: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.6962: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 38ms/step - loss: 0.6962

399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6945: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.6945: end{sphinxVerbatim}

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401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6927: </pre>
textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6927: end{sphinxVerbatim}

[1m401/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6927

403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6910: </pre>
textbf{403/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6910: end{sphinxVerbatim}

[1m403/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6910

405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6893: </pre>
textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6893: end{sphinxVerbatim}

[1m405/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6893

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6875: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6875: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6875

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6858: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6858: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6858

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6842: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6842: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6842

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6833: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6833: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6833

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6816: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6816: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6816

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6800: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6800: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6800

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6783: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6783: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6783

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6767: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6767: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6767

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6751: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 38ms/step - loss: 0.6751: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 38ms/step - loss: 0.6751

422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 41ms/step - loss: 0.6743 - val_loss: 0.0757: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 41ms/step - loss: 0.6743 - val_loss: 0.0757: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 41ms/step - loss: 0.6743 - val_loss: 0.0757

Epoch 2/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 23:49 3s/step - loss: 0.2275: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{23:49} 3s/step - loss: 0.2275: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23:49[0m 3s/step - loss: 0.2275

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textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{11s} 27ms/step - loss: 0.1889: end{sphinxVerbatim}

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textbf{361/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 37ms/step - loss: 0.1234: end{sphinxVerbatim}

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365/422 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step - loss: 0.1233: </pre>
textbf{365/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 37ms/step - loss: 0.1233: end{sphinxVerbatim}

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textbf{375/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 38ms/step - loss: 0.1229: end{sphinxVerbatim}

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textbf{387/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.1225: end{sphinxVerbatim}

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textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.1224: end{sphinxVerbatim}

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textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.1223: end{sphinxVerbatim}

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textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.1223: end{sphinxVerbatim}

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textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.1222: end{sphinxVerbatim}

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397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.1221: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.1221: end{sphinxVerbatim}

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399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.1221: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.1221: end{sphinxVerbatim}

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textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1220: end{sphinxVerbatim}

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textbf{403/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1219: end{sphinxVerbatim}

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textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1218: end{sphinxVerbatim}

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textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1217: end{sphinxVerbatim}

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textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1216: end{sphinxVerbatim}

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textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1216: end{sphinxVerbatim}

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textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1215: end{sphinxVerbatim}

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textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1215: end{sphinxVerbatim}

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418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.1214: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1214: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.1214

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.1213: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.1213

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.1213: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 38ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 38ms/step - loss: 0.1213

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 40ms/step - loss: 0.1212 - val_loss: 0.0552: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 40ms/step - loss: 0.1212 - val_loss: 0.0552: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 40ms/step - loss: 0.1212 - val_loss: 0.0552

Epoch 3/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 25:46 4s/step - loss: 0.1874: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25:46} 4s/step - loss: 0.1874: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25:46[0m 4s/step - loss: 0.1874

 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.1382: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.1382: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.1382

 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.1274: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.1274: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.1274

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textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1213

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textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.1160: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.1160

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textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.1115: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.1115

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textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.1073: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.1073

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textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1040: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1040

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textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1020: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1020

 19/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.1011: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1011: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1011

 21/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.1005: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.1005: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.1005

 22/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.1002: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.1002: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.1002

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textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0996: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0996

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textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0992: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0992

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textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0988: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0988

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textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0985: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0985

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textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0983

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textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0983

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textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 45ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 36/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 45ms/step - loss: 0.0983

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textbf{ 38/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0986: end{sphinxVerbatim}

[1m 38/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0986

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textbf{ 40/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0989: end{sphinxVerbatim}

[1m 40/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0989

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textbf{ 42/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0992: end{sphinxVerbatim}

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textbf{ 44/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0994: end{sphinxVerbatim}

[1m 44/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0994

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textbf{ 46/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0996: end{sphinxVerbatim}

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textbf{ 48/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0996: end{sphinxVerbatim}

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textbf{ 50/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 43ms/step - loss: 0.0996: end{sphinxVerbatim}

[1m 50/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 43ms/step - loss: 0.0996

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textbf{ 52/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0996: end{sphinxVerbatim}

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textbf{ 78/422} textcolor{ansi-green}{━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━} textbf{13s} 39ms/step - loss: 0.0985: end{sphinxVerbatim}

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textbf{ 86/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 39ms/step - loss: 0.0979: end{sphinxVerbatim}

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textbf{ 88/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0978: end{sphinxVerbatim}

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textbf{ 90/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0978: end{sphinxVerbatim}

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textbf{ 92/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0977: end{sphinxVerbatim}

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textbf{ 94/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0976: end{sphinxVerbatim}

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textbf{ 96/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0975: end{sphinxVerbatim}

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0974: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{12s} 38ms/step - loss: 0.0973: end{sphinxVerbatim}

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0972: end{sphinxVerbatim}

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textbf{104/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0972: end{sphinxVerbatim}

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textbf{106/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0971: end{sphinxVerbatim}

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textbf{108/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0970: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0970: end{sphinxVerbatim}

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0969: end{sphinxVerbatim}

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textbf{114/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0968: end{sphinxVerbatim}

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textbf{116/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0967: end{sphinxVerbatim}

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textbf{118/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0966: end{sphinxVerbatim}

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textbf{120/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0965: end{sphinxVerbatim}

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textbf{122/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0964: end{sphinxVerbatim}

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textbf{124/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0963: end{sphinxVerbatim}

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textbf{126/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 37ms/step - loss: 0.0962: end{sphinxVerbatim}

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textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0883: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0883

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0883: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0883: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0883

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0883: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0883: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0883

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0882: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0882: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0882

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0882: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0882: end{sphinxVerbatim}

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

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0882: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0882: end{sphinxVerbatim}

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

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0881: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0881: end{sphinxVerbatim}

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

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0881: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0881: end{sphinxVerbatim}

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

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0881: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 37ms/step - loss: 0.0881: end{sphinxVerbatim}

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

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 39ms/step - loss: 0.0881 - val_loss: 0.0469: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 39ms/step - loss: 0.0881 - val_loss: 0.0469: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 39ms/step - loss: 0.0881 - val_loss: 0.0469

Epoch 4/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 28:24 4s/step - loss: 0.1190: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{28:24} 4s/step - loss: 0.1190: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28:24[0m 4s/step - loss: 0.1190

 2/422 ━━━━━━━━━━━━━━━━━━━━ 21s 50ms/step - loss: 0.1045: </pre>
textbf{ 2/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 50ms/step - loss: 0.1045: end{sphinxVerbatim}

[1m 2/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 50ms/step - loss: 0.1045

 4/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.0981: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.0981: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.0981

 6/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0980: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0980: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0980

 8/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0944: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0944: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0944

 10/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0912: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0912: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0912

 12/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0882: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0882: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0882

 14/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0858: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0858: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0858

 16/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0841: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0841: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0841

 18/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0832: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0832: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0832

 20/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0827: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0827: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0827

 22/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0824: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0824: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0824

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textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0822: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0822

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textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0820: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0820

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textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0819: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0819

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textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0817: end{sphinxVerbatim}

[1m 29/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0817

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textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0816: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0816

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0816: end{sphinxVerbatim}

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textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0816: end{sphinxVerbatim}

[1m 35/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0816

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0817: end{sphinxVerbatim}

[1m 37/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0817

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0820: end{sphinxVerbatim}

[1m 39/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0820

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textbf{ 41/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0822: end{sphinxVerbatim}

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textbf{ 43/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0824: end{sphinxVerbatim}

[1m 43/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0824

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textbf{ 45/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0826: end{sphinxVerbatim}

[1m 45/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 44ms/step - loss: 0.0826

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textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.0732: end{sphinxVerbatim}

[1m388/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.0732

390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.0732: </pre>
textbf{390/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.0732: end{sphinxVerbatim}

[1m390/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.0732

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.0732: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.0732: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.0732

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.0732: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.0732: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.0732

395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.0731: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m395/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.0731

397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0731: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 38ms/step - loss: 0.0731

399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0731: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m399/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 38ms/step - loss: 0.0731

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0731: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 38ms/step - loss: 0.0731

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0731: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0731

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0730: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0730

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0730: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0730

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0730: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0730

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0730: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0730

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0729: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0729

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0729: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0729

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0729: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0729

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0729: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0729

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0729: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0729

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0728: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 39ms/step - loss: 0.0728: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 39ms/step - loss: 0.0728

422/422 ━━━━━━━━━━━━━━━━━━━━ 21s 40ms/step - loss: 0.0728 - val_loss: 0.0424: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 40ms/step - loss: 0.0728 - val_loss: 0.0424: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 40ms/step - loss: 0.0728 - val_loss: 0.0424

Epoch 5/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 24:47 4s/step - loss: 0.0792: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24:47} 4s/step - loss: 0.0792: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24:47[0m 4s/step - loss: 0.0792

 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.0802: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.0802: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.0802

 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - loss: 0.0809: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 47ms/step - loss: 0.0809: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 47ms/step - loss: 0.0809

 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0797: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0797: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0797

 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0775: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0775: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0775

 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0755: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0755: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0755

 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0738: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0738: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0738

 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0721: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0721: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0721

 17/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0711: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0711: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0711

 19/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0706: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0706: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0706

 21/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0704: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0704

 23/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0704: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0704

 25/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0702: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0702

 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0702: </pre>
textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0702

 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0702: </pre>
textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0702: end{sphinxVerbatim}

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textbf{ 98/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 35ms/step - loss: 0.0689: end{sphinxVerbatim}

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textbf{ 99/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0689: end{sphinxVerbatim}

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textbf{101/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0688: end{sphinxVerbatim}

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textbf{103/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0687: end{sphinxVerbatim}

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textbf{105/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{107/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{108/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 36ms/step - loss: 0.0686: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0685: end{sphinxVerbatim}

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0685: end{sphinxVerbatim}

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textbf{113/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{115/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0684: end{sphinxVerbatim}

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textbf{117/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0683: end{sphinxVerbatim}

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119/422 ━━━━━━━━━━━━━━━━━━━━ 11s 37ms/step - loss: 0.0683: </pre>
textbf{119/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0683: end{sphinxVerbatim}

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0682: end{sphinxVerbatim}

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textbf{123/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0682: end{sphinxVerbatim}

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textbf{125/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0681: end{sphinxVerbatim}

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textbf{127/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0681: end{sphinxVerbatim}

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textbf{129/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 37ms/step - loss: 0.0681: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0681: end{sphinxVerbatim}

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textbf{133/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0680: end{sphinxVerbatim}

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textbf{135/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0680: end{sphinxVerbatim}

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textbf{137/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0680: end{sphinxVerbatim}

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textbf{139/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0679: end{sphinxVerbatim}

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textbf{141/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0679: end{sphinxVerbatim}

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textbf{143/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0679: end{sphinxVerbatim}

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textbf{145/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0678: end{sphinxVerbatim}

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textbf{147/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0678: end{sphinxVerbatim}

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textbf{149/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0678: end{sphinxVerbatim}

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textbf{151/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0677: end{sphinxVerbatim}

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textbf{152/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0677: end{sphinxVerbatim}

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textbf{154/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0677: end{sphinxVerbatim}

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textbf{156/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0676: end{sphinxVerbatim}

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textbf{158/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0676: end{sphinxVerbatim}

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textbf{160/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 39ms/step - loss: 0.0676: end{sphinxVerbatim}

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textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0648: end{sphinxVerbatim}

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textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0648: end{sphinxVerbatim}

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397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0647: </pre>
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399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0647: </pre>
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401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0647: </pre>
textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0647: end{sphinxVerbatim}

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textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0647: end{sphinxVerbatim}

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407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0647: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0647: end{sphinxVerbatim}

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409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0647: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0647: end{sphinxVerbatim}

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411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0646

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 38ms/step - loss: 0.0646 - val_loss: 0.0371: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 38ms/step - loss: 0.0646 - val_loss: 0.0371: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 38ms/step - loss: 0.0646 - val_loss: 0.0371

Epoch 6/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 30:10 4s/step - loss: 0.0920: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{30:10} 4s/step - loss: 0.0920: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30:10[0m 4s/step - loss: 0.0920

 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0708: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0708: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0708

 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0656: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0656: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0656

 7/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0629: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0629

 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0598: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0598: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0598

 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0568: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0568: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0568

 13/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0548: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0548: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0548

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textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0535: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0535

 17/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0533: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0533

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{12s} 41ms/step - loss: 0.0600: end{sphinxVerbatim}

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[1m378/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 36ms/step - loss: 0.0590

380/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0590: </pre>
textbf{380/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0590: end{sphinxVerbatim}

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382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0590: </pre>
textbf{382/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0590: end{sphinxVerbatim}

[1m382/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0590

384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m384/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m386/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m388/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m389/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 36ms/step - loss: 0.0589: </pre>
textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m394/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 36ms/step - loss: 0.0589

396/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0589: </pre>
textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m396/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 36ms/step - loss: 0.0589

398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0589: </pre>
textbf{398/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m398/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 36ms/step - loss: 0.0589

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0589: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 36ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 36ms/step - loss: 0.0589

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m405/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0588: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0588

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - loss: 0.0587: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 36ms/step - loss: 0.0587: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 36ms/step - loss: 0.0587

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 38ms/step - loss: 0.0587 - val_loss: 0.0357: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 38ms/step - loss: 0.0587 - val_loss: 0.0357: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 38ms/step - loss: 0.0587 - val_loss: 0.0357

Epoch 7/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 26s 62ms/step - loss: 0.0952: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{26s} 62ms/step - loss: 0.0952: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 62ms/step - loss: 0.0952

 3/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.0782: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.0782: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.0782

 5/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.0759: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.0759: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.0759

 7/422 ━━━━━━━━━━━━━━━━━━━━ 19s 46ms/step - loss: 0.0742: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 46ms/step - loss: 0.0742: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 46ms/step - loss: 0.0742

 9/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0712: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0712: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0712

 11/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0681: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0681: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0681

 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 44ms/step - loss: 0.0655: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0655: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0655

 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0635: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0635: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0635

 17/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0625: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0625: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0625

 19/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0621: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0621: end{sphinxVerbatim}

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textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m409/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

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textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m411/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

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textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

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textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

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textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

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421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0554: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0554

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0554: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 42ms/step - loss: 0.0554

422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - loss: 0.0554 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 43ms/step - loss: 0.0554 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 43ms/step - loss: 0.0554 - val_loss: 0.0342

Epoch 8/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 15:07 2s/step - loss: 0.0872: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{15:07} 2s/step - loss: 0.0872: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:07[0m 2s/step - loss: 0.0872

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textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0774: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0774

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textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 43ms/step - loss: 0.0760: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 43ms/step - loss: 0.0760

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textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 46ms/step - loss: 0.0757: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 46ms/step - loss: 0.0757

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textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0737: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0737

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textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0725: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0725

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textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0697: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0697

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textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0672: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0672

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textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0650: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0650

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textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0640: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0640

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textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0634: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0634

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textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0629

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textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0623: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.0623

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textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0616: end{sphinxVerbatim}

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textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0613: end{sphinxVerbatim}

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textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0611: end{sphinxVerbatim}

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textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0608: end{sphinxVerbatim}

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0604: end{sphinxVerbatim}

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textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.0603: end{sphinxVerbatim}

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 44ms/step - loss: 0.0601: end{sphinxVerbatim}

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textbf{ 45/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0600: end{sphinxVerbatim}

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textbf{ 50/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{14s} 40ms/step - loss: 0.0596: end{sphinxVerbatim}

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textbf{ 56/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{13s} 38ms/step - loss: 0.0592: end{sphinxVerbatim}

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textbf{ 79/422} textcolor{ansi-green}{━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━} textbf{11s} 35ms/step - loss: 0.0571: end{sphinxVerbatim}

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textbf{ 87/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 34ms/step - loss: 0.0566: end{sphinxVerbatim}

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textbf{ 90/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{11s} 34ms/step - loss: 0.0565: end{sphinxVerbatim}

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textbf{100/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{10s} 33ms/step - loss: 0.0561: end{sphinxVerbatim}

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textbf{102/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{10s} 33ms/step - loss: 0.0561: end{sphinxVerbatim}

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textbf{105/422} textcolor{ansi-green}{━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━} textbf{10s} 32ms/step - loss: 0.0560: end{sphinxVerbatim}

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textbf{107/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 32ms/step - loss: 0.0560: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{9s} 32ms/step - loss: 0.0559: end{sphinxVerbatim}

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textbf{112/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{9s} 32ms/step - loss: 0.0558: end{sphinxVerbatim}

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textbf{121/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{9s} 32ms/step - loss: 0.0556: end{sphinxVerbatim}

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textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0525

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0525: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0525

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0525: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0525

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0525: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0525

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0524: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0524

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0524: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0524

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0524: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0524

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0524: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0524

422/422 ━━━━━━━━━━━━━━━━━━━━ 17s 36ms/step - loss: 0.0524 - val_loss: 0.0331: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 36ms/step - loss: 0.0524 - val_loss: 0.0331: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 36ms/step - loss: 0.0524 - val_loss: 0.0331

Epoch 9/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - loss: 0.0569: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 41ms/step - loss: 0.0569: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 41ms/step - loss: 0.0569

 3/422 ━━━━━━━━━━━━━━━━━━━━ 11s 27ms/step - loss: 0.0630: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{11s} 27ms/step - loss: 0.0630: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 27ms/step - loss: 0.0630

 5/422 ━━━━━━━━━━━━━━━━━━━━ 11s 27ms/step - loss: 0.0619: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{11s} 27ms/step - loss: 0.0619: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 27ms/step - loss: 0.0619

 7/422 ━━━━━━━━━━━━━━━━━━━━ 11s 27ms/step - loss: 0.0602: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{11s} 27ms/step - loss: 0.0602: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 27ms/step - loss: 0.0602

 9/422 ━━━━━━━━━━━━━━━━━━━━ 10s 26ms/step - loss: 0.0579: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0579: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0579

 11/422 ━━━━━━━━━━━━━━━━━━━━ 10s 26ms/step - loss: 0.0562: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0562: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0562

 13/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0550: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0550: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0550

 15/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0539: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0539: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0539

 17/422 ━━━━━━━━━━━━━━━━━━━━ 11s 27ms/step - loss: 0.0534: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{11s} 27ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 27ms/step - loss: 0.0534

 20/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0534: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0534

 22/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0533: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0533

 24/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0532: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0532: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0532

 26/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0530: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0530

 28/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0529: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0529

 30/422 ━━━━━━━━━━━━━━━━━━━━ 10s 27ms/step - loss: 0.0530: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0530

 33/422 ━━━━━━━━━━━━━━━━━━━━ 10s 26ms/step - loss: 0.0529: </pre>
textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0529

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textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0530: end{sphinxVerbatim}

[1m 36/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0530

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textbf{ 38/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0531: end{sphinxVerbatim}

[1m 38/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0531

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textbf{ 40/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{10s} 26ms/step - loss: 0.0533: end{sphinxVerbatim}

[1m 40/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 26ms/step - loss: 0.0533

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textbf{ 42/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{9s} 26ms/step - loss: 0.0534: end{sphinxVerbatim}

[1m 42/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - loss: 0.0534

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textbf{ 44/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{9s} 26ms/step - loss: 0.0536: end{sphinxVerbatim}

[1m 44/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - loss: 0.0536

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textbf{ 47/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{9s} 26ms/step - loss: 0.0537: end{sphinxVerbatim}

[1m 47/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - loss: 0.0537

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textbf{ 49/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{10s} 27ms/step - loss: 0.0537: end{sphinxVerbatim}

[1m 49/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 27ms/step - loss: 0.0537

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textbf{ 51/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{10s} 28ms/step - loss: 0.0537: end{sphinxVerbatim}

[1m 51/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 28ms/step - loss: 0.0537

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textbf{ 53/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{10s} 28ms/step - loss: 0.0537: end{sphinxVerbatim}

[1m 53/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 28ms/step - loss: 0.0537

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[1m398/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 37ms/step - loss: 0.0492

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 37ms/step - loss: 0.0492

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0492: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0492

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0491: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0491

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0491: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0491

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0491: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0491

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0491: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 37ms/step - loss: 0.0491

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0491: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 37ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 37ms/step - loss: 0.0491

422/422 ━━━━━━━━━━━━━━━━━━━━ 16s 39ms/step - loss: 0.0491 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 39ms/step - loss: 0.0491 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 39ms/step - loss: 0.0491 - val_loss: 0.0342

Epoch 10/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 64ms/step - loss: 0.0320: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 64ms/step - loss: 0.0320: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 64ms/step - loss: 0.0320

 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - loss: 0.0375: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 43ms/step - loss: 0.0375: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 43ms/step - loss: 0.0375

 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0399: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0399: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0399

 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0404: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0404: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0404

 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0401: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0401: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0401

 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0396: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0396: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0396

 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0388: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0388: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0388

 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0383: </pre>
textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0383: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0383

 17/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0388: </pre>
textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0388: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0388

 19/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0397: </pre>
textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0397: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0397

 21/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0404: </pre>
textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0404: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0404

 23/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0410: </pre>
textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0410: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0410

 25/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0414: </pre>
textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0414: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0414

 27/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0419: </pre>
textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0419: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0419

 29/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0424: </pre>
textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0424: end{sphinxVerbatim}

[1m 29/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0424

 31/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0429: </pre>
textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0429: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0429

 33/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0433: </pre>
textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0433: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0433

 35/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0437: </pre>
textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0437: end{sphinxVerbatim}

[1m 35/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0437

 37/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0441: </pre>
textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0441: end{sphinxVerbatim}

[1m 37/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0441

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0445: end{sphinxVerbatim}

[1m 39/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0445

 41/422 ━━━━━━━━━━━━━━━━━━━━ 16s 43ms/step - loss: 0.0449: </pre>
textbf{ 41/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0449: end{sphinxVerbatim}

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textbf{160/422} textcolor{ansi-green}{━━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━} textbf{10s} 38ms/step - loss: 0.0464: end{sphinxVerbatim}

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[33]:

MultiDimensionalClassifier(
    model=<function get_model at 0x7febf397d620>
    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