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, :])

[[0.4807934 ]
 [0.46760356]]

[11]:

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

[[0.17575906 0.22164693 0.18973085 0.17472962 0.23813365]
 [0.15294217 0.20687361 0.22993892 0.19339739 0.21684793]]

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([1, 1, 0, 0]), array([4, 3, 1, 3])]

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

[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]:

0.445

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]:

-3.8483239268266676

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

4.2 Define data transformer¶

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

[23]:

from sklearn.preprocessing import FunctionTransformer


class MultiInputRegressor(KerasRegressor):

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

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

4.3 Test regressor¶

[24]:

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

X_sklearn = np.column_stack(X)

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

[24]:

-3.578607815360688

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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[1m361/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 39ms/step - loss: 0.7303

363/422 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step - loss: 0.7282: </pre>
textbf{363/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 39ms/step - loss: 0.7282: end{sphinxVerbatim}

[1m363/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 39ms/step - loss: 0.7282

365/422 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step - loss: 0.7262: </pre>
textbf{365/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 39ms/step - loss: 0.7262: end{sphinxVerbatim}

[1m365/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 39ms/step - loss: 0.7262

368/422 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step - loss: 0.7233: </pre>
textbf{368/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 39ms/step - loss: 0.7233: end{sphinxVerbatim}

[1m368/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 39ms/step - loss: 0.7233

371/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7203: </pre>
textbf{371/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 39ms/step - loss: 0.7203: end{sphinxVerbatim}

[1m371/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 39ms/step - loss: 0.7203

373/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7184: </pre>
textbf{373/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 39ms/step - loss: 0.7184: end{sphinxVerbatim}

[1m373/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 39ms/step - loss: 0.7184

376/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7155: </pre>
textbf{376/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 39ms/step - loss: 0.7155: end{sphinxVerbatim}

[1m376/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 39ms/step - loss: 0.7155

379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7127: </pre>
textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 39ms/step - loss: 0.7127: end{sphinxVerbatim}

[1m379/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 39ms/step - loss: 0.7127

381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7108: </pre>
textbf{381/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 39ms/step - loss: 0.7108: end{sphinxVerbatim}

[1m381/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 39ms/step - loss: 0.7108

384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.7080: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 39ms/step - loss: 0.7080: end{sphinxVerbatim}

[1m384/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 39ms/step - loss: 0.7080

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

392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.7007: </pre>
textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.7007: end{sphinxVerbatim}

[1m392/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.7007

394/422 ━━━━━━━━━━━━━━━━━━━━ 1s 38ms/step - loss: 0.6989: </pre>
textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 38ms/step - loss: 0.6989: end{sphinxVerbatim}

[1m394/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 38ms/step - loss: 0.6989

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

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6936: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 38ms/step - loss: 0.6936: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 38ms/step - loss: 0.6936

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

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6825: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6825: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6825

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6808: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6808: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6808

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6792: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6792: end{sphinxVerbatim}

[1m417/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6792

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6775: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6775: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6775

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.6759: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.6759: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.6759

422/422 ━━━━━━━━━━━━━━━━━━━━ 18s 40ms/step - loss: 0.6743 - val_loss: 0.0757: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 40ms/step - loss: 0.6743 - val_loss: 0.0757: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 40ms/step - loss: 0.6743 - val_loss: 0.0757

Epoch 2/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 27s 64ms/step - loss: 0.2275: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{27s} 64ms/step - loss: 0.2275: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 64ms/step - loss: 0.2275

 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.1889: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.1889: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.1889

 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.1763: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1763: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1763

 7/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.1668: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.1668: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.1668

 9/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.1628: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.1628: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.1628

 11/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.1587: </pre>
textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.1587: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.1587

 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.1545: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.1545: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.1545

 15/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.1507: </pre>
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textbf{350/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 40ms/step - loss: 0.1238: end{sphinxVerbatim}

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textbf{352/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 40ms/step - loss: 0.1238: end{sphinxVerbatim}

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textbf{354/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 40ms/step - loss: 0.1237: end{sphinxVerbatim}

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textbf{356/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 40ms/step - loss: 0.1236: end{sphinxVerbatim}

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textbf{358/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 40ms/step - loss: 0.1235: end{sphinxVerbatim}

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360/422 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step - loss: 0.1235: </pre>
textbf{360/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 40ms/step - loss: 0.1235: end{sphinxVerbatim}

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361/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.1234: </pre>
textbf{361/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1234: end{sphinxVerbatim}

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textbf{363/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1233: end{sphinxVerbatim}

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365/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.1233: </pre>
textbf{365/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1233: end{sphinxVerbatim}

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367/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.1232: </pre>
textbf{367/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1232: end{sphinxVerbatim}

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369/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.1231: </pre>
textbf{369/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1231: end{sphinxVerbatim}

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371/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.1230: </pre>
textbf{371/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.1230: end{sphinxVerbatim}

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373/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.1230: </pre>
textbf{373/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.1230: end{sphinxVerbatim}

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375/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.1229: </pre>
textbf{375/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.1229: end{sphinxVerbatim}

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377/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.1228: </pre>
textbf{377/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.1228: end{sphinxVerbatim}

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379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.1228: </pre>
textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 40ms/step - loss: 0.1228: end{sphinxVerbatim}

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382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.1227: </pre>
textbf{382/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1227: end{sphinxVerbatim}

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384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.1226: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1226: end{sphinxVerbatim}

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387/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.1225: </pre>
textbf{387/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1225: end{sphinxVerbatim}

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390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.1224: </pre>
textbf{390/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1224: end{sphinxVerbatim}

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textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1223: end{sphinxVerbatim}

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textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.1222: end{sphinxVerbatim}

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397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.1221: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 40ms/step - loss: 0.1221: end{sphinxVerbatim}

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400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.1220: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 40ms/step - loss: 0.1220: end{sphinxVerbatim}

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402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.1219: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.1219: end{sphinxVerbatim}

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404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.1219: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.1219: end{sphinxVerbatim}

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407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1218: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1218: end{sphinxVerbatim}

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409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1217: </pre>
textbf{409/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1217: end{sphinxVerbatim}

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411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1216: </pre>
textbf{411/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1216: end{sphinxVerbatim}

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textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1216: end{sphinxVerbatim}

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415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1215: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1215: end{sphinxVerbatim}

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417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1214: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1214: end{sphinxVerbatim}

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419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1214: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1214: end{sphinxVerbatim}

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421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.1213: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.1213: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.1213

422/422 ━━━━━━━━━━━━━━━━━━━━ 17s 40ms/step - loss: 0.1212 - val_loss: 0.0552: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 40ms/step - loss: 0.1212 - val_loss: 0.0552: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 40ms/step - loss: 0.1212 - val_loss: 0.0552

Epoch 3/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 25:57 4s/step - loss: 0.1874: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{25:57} 4s/step - loss: 0.1874: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25:57[0m 4s/step - loss: 0.1874

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textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.1382: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.1382

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textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.1274: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/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{17s} 43ms/step - loss: 0.1115: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1115

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textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 44ms/step - loss: 0.1094: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 44ms/step - loss: 0.1094

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textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1028: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1028

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textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1016: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1016

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textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1008: end{sphinxVerbatim}

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textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.1002: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.1002

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[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0996

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textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0992: end{sphinxVerbatim}

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textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0988: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0988

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textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0985: end{sphinxVerbatim}

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textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0983: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 43ms/step - loss: 0.0983

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textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 43ms/step - loss: 0.0983: end{sphinxVerbatim}

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textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0884: end{sphinxVerbatim}

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textbf{405/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0884: end{sphinxVerbatim}

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[1m421/422[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 ━━━━━━━━━━━━━━━━━━━━ 28:41 4s/step - loss: 0.1190: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{28:41} 4s/step - loss: 0.1190: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28:41[0m 4s/step - loss: 0.1190

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textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 38ms/step - loss: 0.1013: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 38ms/step - loss: 0.1013

 5/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.0992: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0992: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0992

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textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0963: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0963

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textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0926: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0926

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textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0896: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0896

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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

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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

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textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0841: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0841

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textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0832: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0832

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textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0827: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0827

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textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0824: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0824

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textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0822: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0822

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textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0820: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0820

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textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0818: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0818

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textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0817: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0817

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textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0815: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0815

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textbf{ 34/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0816: end{sphinxVerbatim}

[1m 34/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0816

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textbf{ 36/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0816: end{sphinxVerbatim}

[1m 36/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0816

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textbf{ 38/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0819: end{sphinxVerbatim}

[1m 38/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0819

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textbf{ 40/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0821: end{sphinxVerbatim}

[1m 40/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 42ms/step - loss: 0.0821

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textbf{ 42/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0823: end{sphinxVerbatim}

[1m 42/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 42ms/step - loss: 0.0823

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textbf{ 44/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0825: end{sphinxVerbatim}

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textbf{ 46/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0826: end{sphinxVerbatim}

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textbf{ 48/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0826: end{sphinxVerbatim}

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textbf{ 50/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0825: end{sphinxVerbatim}

[1m 50/422[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 41ms/step - loss: 0.0825

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textbf{ 52/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0824: end{sphinxVerbatim}

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textbf{ 54/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0823: end{sphinxVerbatim}

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textbf{ 56/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0822: end{sphinxVerbatim}

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textbf{ 58/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0821: end{sphinxVerbatim}

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textbf{ 60/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{14s} 41ms/step - loss: 0.0819: end{sphinxVerbatim}

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textbf{ 62/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{14s} 41ms/step - loss: 0.0818: end{sphinxVerbatim}

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textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0731: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0731

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0730: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0730

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0730: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0730

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0730: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0730

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0730: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0730: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0730

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0729: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0729

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0729: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0729

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0729: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0729

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0729: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0729

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0729: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0729

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0728: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 40ms/step - loss: 0.0728: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 40ms/step - loss: 0.0728

422/422 ━━━━━━━━━━━━━━━━━━━━ 21s 41ms/step - loss: 0.0728 - val_loss: 0.0424: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 41ms/step - loss: 0.0728 - val_loss: 0.0424: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 41ms/step - loss: 0.0728 - val_loss: 0.0424

Epoch 5/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 30s 73ms/step - loss: 0.0792: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{30s} 73ms/step - loss: 0.0792: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 73ms/step - loss: 0.0792

 3/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.0802: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0802: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0802

 5/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - loss: 0.0809: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 41ms/step - loss: 0.0809: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 41ms/step - loss: 0.0809

 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 39ms/step - loss: 0.0797: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 39ms/step - loss: 0.0797: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 39ms/step - loss: 0.0797

 8/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0787: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0787: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0787

 10/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - loss: 0.0766: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 41ms/step - loss: 0.0766: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 41ms/step - loss: 0.0766

 12/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0747: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0747: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0747

 14/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0729: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0729: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0729

 16/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0715: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0715: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0715

 18/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0709: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0709: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0709

 20/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0705: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0705: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0705

 22/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0704: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0704

 24/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0703: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0703: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0703

 26/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0703: </pre>
textbf{ 26/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0703: end{sphinxVerbatim}

[1m 26/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0703

 28/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0702: </pre>
textbf{ 28/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 28/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0702

 30/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0702: </pre>
textbf{ 30/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 30/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0702

 32/422 ━━━━━━━━━━━━━━━━━━━━ 15s 41ms/step - loss: 0.0701: </pre>
textbf{ 32/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0701: end{sphinxVerbatim}

[1m 32/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 41ms/step - loss: 0.0701

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0702: end{sphinxVerbatim}

[1m 33/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0702

 35/422 ━━━━━━━━━━━━━━━━━━━━ 16s 41ms/step - loss: 0.0703: </pre>
textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0703: end{sphinxVerbatim}

[1m 35/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 41ms/step - loss: 0.0703

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0704: end{sphinxVerbatim}

[1m 37/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 41ms/step - loss: 0.0704

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0707: end{sphinxVerbatim}

[1m 39/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 41ms/step - loss: 0.0707

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textbf{ 41/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0708: end{sphinxVerbatim}

[1m 41/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 41ms/step - loss: 0.0708

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textbf{ 43/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0710: end{sphinxVerbatim}

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

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

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

417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step - loss: 0.0646: </pre>
textbf{417/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

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

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textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 37ms/step - loss: 0.0646: end{sphinxVerbatim}

[1m419/422[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 ━━━━━━━━━━━━━━━━━━━━ 16s 38ms/step - loss: 0.0646 - val_loss: 0.0371: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 38ms/step - loss: 0.0646 - val_loss: 0.0371: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 38ms/step - loss: 0.0646 - val_loss: 0.0371

Epoch 6/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 30:57 4s/step - loss: 0.0920: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{30:57} 4s/step - loss: 0.0920: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30:57[0m 4s/step - loss: 0.0920

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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

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textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0656: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0656

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textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0629: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0629

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textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0598: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0598

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textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0568: end{sphinxVerbatim}

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textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0548: end{sphinxVerbatim}

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textbf{ 15/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0535: end{sphinxVerbatim}

[1m 15/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0535

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[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0533

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textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0534: end{sphinxVerbatim}

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textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0540: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0540

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textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0545: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0545

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textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0548: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0548

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0564: end{sphinxVerbatim}

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0569: end{sphinxVerbatim}

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 41ms/step - loss: 0.0573: end{sphinxVerbatim}

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[1m394/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 39ms/step - loss: 0.0589

396/422 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step - loss: 0.0589: </pre>
textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 39ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m396/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 39ms/step - loss: 0.0589

399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0589: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 39ms/step - loss: 0.0589: end{sphinxVerbatim}

[1m399/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 39ms/step - loss: 0.0589

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0588: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0588

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0588: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0588

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0588: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0588

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0588: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0588

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0588: </pre>
textbf{413/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m413/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.0588

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0588: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.0588

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0588: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.0588: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.0588

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 38ms/step - loss: 0.0587: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 38ms/step - loss: 0.0587: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 38ms/step - loss: 0.0587

422/422 ━━━━━━━━━━━━━━━━━━━━ 21s 39ms/step - loss: 0.0587 - val_loss: 0.0357: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{21s} 39ms/step - loss: 0.0587 - val_loss: 0.0357: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 39ms/step - loss: 0.0587 - val_loss: 0.0357

Epoch 7/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 28:11 4s/step - loss: 0.0952: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{28:11} 4s/step - loss: 0.0952: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28:11[0m 4s/step - loss: 0.0952

 3/422 ━━━━━━━━━━━━━━━━━━━━ 17s 41ms/step - loss: 0.0782: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 41ms/step - loss: 0.0782: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 41ms/step - loss: 0.0782

 5/422 ━━━━━━━━━━━━━━━━━━━━ 18s 43ms/step - loss: 0.0759: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 43ms/step - loss: 0.0759: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 43ms/step - loss: 0.0759

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textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0742: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0742

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textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0712: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0712

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textbf{ 11/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0681: end{sphinxVerbatim}

[1m 11/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0681

 13/422 ━━━━━━━━━━━━━━━━━━━━ 17s 43ms/step - loss: 0.0655: </pre>
textbf{ 13/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 43ms/step - loss: 0.0655: end{sphinxVerbatim}

[1m 13/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 43ms/step - loss: 0.0655

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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

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textbf{ 17/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0625: end{sphinxVerbatim}

[1m 17/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0625

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textbf{ 19/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0621: end{sphinxVerbatim}

[1m 19/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0621

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textbf{ 21/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0619: end{sphinxVerbatim}

[1m 21/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0619

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textbf{ 23/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0617: end{sphinxVerbatim}

[1m 23/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0617

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textbf{ 25/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0614: end{sphinxVerbatim}

[1m 25/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0614

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textbf{ 27/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0613: end{sphinxVerbatim}

[1m 27/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0613

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textbf{ 29/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 41ms/step - loss: 0.0611: end{sphinxVerbatim}

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textbf{ 31/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0609: end{sphinxVerbatim}

[1m 31/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0609

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textbf{ 33/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0607: end{sphinxVerbatim}

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textbf{ 35/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0606: end{sphinxVerbatim}

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textbf{ 37/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0605: end{sphinxVerbatim}

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textbf{ 39/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0606: end{sphinxVerbatim}

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textbf{ 41/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0607: end{sphinxVerbatim}

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textbf{ 43/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0607: end{sphinxVerbatim}

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textbf{ 45/422} textcolor{ansi-green}{━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━} textbf{15s} 42ms/step - loss: 0.0608: end{sphinxVerbatim}

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textbf{374/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m374/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 43ms/step - loss: 0.0557

376/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{376/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m376/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 43ms/step - loss: 0.0557

378/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{378/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m378/422[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 43ms/step - loss: 0.0557

380/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{380/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m380/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - loss: 0.0557

382/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{382/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m382/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - loss: 0.0557

384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m384/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - loss: 0.0557

386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0557: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 43ms/step - loss: 0.0557: end{sphinxVerbatim}

[1m386/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - loss: 0.0557

388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step - loss: 0.0556: </pre>
textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 43ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m388/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 43ms/step - loss: 0.0556

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - loss: 0.0556: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 42ms/step - loss: 0.0556

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - loss: 0.0556: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 42ms/step - loss: 0.0556

395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - loss: 0.0556: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m395/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 42ms/step - loss: 0.0556

397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 42ms/step - loss: 0.0556: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 42ms/step - loss: 0.0556

399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0556: </pre>
textbf{399/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m399/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 42ms/step - loss: 0.0556

401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0556: </pre>
textbf{401/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0556: end{sphinxVerbatim}

[1m401/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0556

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

413/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
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

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0555: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0555: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0555

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0554: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0554

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 42ms/step - loss: 0.0554: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 42ms/step - loss: 0.0554: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 42ms/step - loss: 0.0554

422/422 ━━━━━━━━━━━━━━━━━━━━ 22s 42ms/step - loss: 0.0554 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{22s} 42ms/step - loss: 0.0554 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 42ms/step - loss: 0.0554 - val_loss: 0.0342

Epoch 8/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 18:19 3s/step - loss: 0.0872: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18:19} 3s/step - loss: 0.0872: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18:19[0m 3s/step - loss: 0.0872

 3/422 ━━━━━━━━━━━━━━━━━━━━ 18s 44ms/step - loss: 0.0774: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 44ms/step - loss: 0.0774: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 44ms/step - loss: 0.0774

 4/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - loss: 0.0733: </pre>
textbf{ 4/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 47ms/step - loss: 0.0733: end{sphinxVerbatim}

[1m 4/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 47ms/step - loss: 0.0733

 5/422 ━━━━━━━━━━━━━━━━━━━━ 20s 49ms/step - loss: 0.0760: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 49ms/step - loss: 0.0760: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 49ms/step - loss: 0.0760

 6/422 ━━━━━━━━━━━━━━━━━━━━ 20s 50ms/step - loss: 0.0757: </pre>
textbf{ 6/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 50ms/step - loss: 0.0757: end{sphinxVerbatim}

[1m 6/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 50ms/step - loss: 0.0757

 8/422 ━━━━━━━━━━━━━━━━━━━━ 20s 48ms/step - loss: 0.0737: </pre>
textbf{ 8/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 48ms/step - loss: 0.0737: end{sphinxVerbatim}

[1m 8/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 48ms/step - loss: 0.0737

 10/422 ━━━━━━━━━━━━━━━━━━━━ 19s 47ms/step - loss: 0.0711: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 47ms/step - loss: 0.0711: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 47ms/step - loss: 0.0711

 12/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - loss: 0.0685: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 46ms/step - loss: 0.0685: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 46ms/step - loss: 0.0685

 14/422 ━━━━━━━━━━━━━━━━━━━━ 18s 46ms/step - loss: 0.0660: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 46ms/step - loss: 0.0660: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 46ms/step - loss: 0.0660

 16/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0644: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0644: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0644

 18/422 ━━━━━━━━━━━━━━━━━━━━ 18s 45ms/step - loss: 0.0637: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{18s} 45ms/step - loss: 0.0637: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 45ms/step - loss: 0.0637

 20/422 ━━━━━━━━━━━━━━━━━━━━ 17s 45ms/step - loss: 0.0632: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 45ms/step - loss: 0.0632: end{sphinxVerbatim}

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[1m351/422[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 41ms/step - loss: 0.0530

353/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{353/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m353/422[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 41ms/step - loss: 0.0529

355/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{355/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m355/422[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 41ms/step - loss: 0.0529

357/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{357/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

[1m357/422[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 41ms/step - loss: 0.0529

359/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{359/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

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360/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{360/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

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361/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0529: </pre>
textbf{361/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0529: end{sphinxVerbatim}

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363/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0528: </pre>
textbf{363/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0528: end{sphinxVerbatim}

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364/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0528: </pre>
textbf{364/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0528: end{sphinxVerbatim}

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366/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0528: </pre>
textbf{366/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0528: end{sphinxVerbatim}

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369/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0528: </pre>
textbf{369/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0528: end{sphinxVerbatim}

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372/422 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step - loss: 0.0528: </pre>
textbf{372/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{2s} 41ms/step - loss: 0.0528: end{sphinxVerbatim}

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375/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{375/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

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377/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{377/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

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379/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{379/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

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381/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{381/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

[1m381/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 41ms/step - loss: 0.0527

384/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

[1m384/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 41ms/step - loss: 0.0527

386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 41ms/step - loss: 0.0527: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 41ms/step - loss: 0.0527: end{sphinxVerbatim}

[1m386/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 41ms/step - loss: 0.0527

389/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.0526: </pre>
textbf{389/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.0526: end{sphinxVerbatim}

[1m389/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 40ms/step - loss: 0.0526

391/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.0526: </pre>
textbf{391/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.0526: end{sphinxVerbatim}

[1m391/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 40ms/step - loss: 0.0526

393/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.0526: </pre>
textbf{393/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.0526: end{sphinxVerbatim}

[1m393/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 40ms/step - loss: 0.0526

395/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.0526: </pre>
textbf{395/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.0526: end{sphinxVerbatim}

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397/422 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step - loss: 0.0526: </pre>
textbf{397/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 40ms/step - loss: 0.0526: end{sphinxVerbatim}

[1m397/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 40ms/step - loss: 0.0526

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0525: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 40ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 40ms/step - loss: 0.0525

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0525: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0525

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0525: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0525

407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0525: </pre>
textbf{407/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m407/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0525

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0525: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0525: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0525

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0524: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0524

415/422 ━━━━━━━━━━━━━━━━━━━━ 0s 40ms/step - loss: 0.0524: </pre>
textbf{415/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 40ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m415/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 40ms/step - loss: 0.0524

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0524: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0524

420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0524: </pre>
textbf{420/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 39ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m420/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 39ms/step - loss: 0.0524

422/422 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - loss: 0.0524: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{0s} 39ms/step - loss: 0.0524: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 39ms/step - loss: 0.0524

422/422 ━━━━━━━━━━━━━━━━━━━━ 20s 40ms/step - loss: 0.0524 - val_loss: 0.0331: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{20s} 40ms/step - loss: 0.0524 - val_loss: 0.0331: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 40ms/step - loss: 0.0524 - val_loss: 0.0331

Epoch 9/10

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textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{24:49} 4s/step - loss: 0.0569: end{sphinxVerbatim}

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textbf{110/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 38ms/step - loss: 0.0519: end{sphinxVerbatim}

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textbf{113/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{11s} 37ms/step - loss: 0.0519: end{sphinxVerbatim}

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textbf{125/422} textcolor{ansi-green}{━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{128/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 36ms/step - loss: 0.0517: end{sphinxVerbatim}

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textbf{131/422} textcolor{ansi-green}{━━━━━━}textcolor{ansi-white}{━━━━━━━━━━━━━━} textbf{10s} 35ms/step - loss: 0.0516: end{sphinxVerbatim}

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textbf{384/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m384/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 34ms/step - loss: 0.0493

386/422 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - loss: 0.0493: </pre>
textbf{386/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m386/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 34ms/step - loss: 0.0493

388/422 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - loss: 0.0493: </pre>
textbf{388/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m388/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 34ms/step - loss: 0.0493

390/422 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - loss: 0.0493: </pre>
textbf{390/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m390/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 34ms/step - loss: 0.0493

392/422 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - loss: 0.0493: </pre>
textbf{392/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{1s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m392/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 34ms/step - loss: 0.0493

394/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0493: </pre>
textbf{394/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m394/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step - loss: 0.0493

396/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0493: </pre>
textbf{396/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 34ms/step - loss: 0.0493: end{sphinxVerbatim}

[1m396/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step - loss: 0.0493

398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{398/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m398/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step - loss: 0.0492

400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{400/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m400/422[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 34ms/step - loss: 0.0492

402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{402/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m402/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{404/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m404/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{406/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m406/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{408/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m408/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{410/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m410/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0492: </pre>
textbf{412/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0492: end{sphinxVerbatim}

[1m412/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0492

414/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0491: </pre>
textbf{414/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m414/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0491

416/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0491: </pre>
textbf{416/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m416/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0491

418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 34ms/step - loss: 0.0491: </pre>
textbf{418/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 34ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m418/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 34ms/step - loss: 0.0491

419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0491: </pre>
textbf{419/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m419/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0491

421/422 ━━━━━━━━━━━━━━━━━━━━ 0s 35ms/step - loss: 0.0491: </pre>
textbf{421/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━}textcolor{ansi-white}{━} textbf{0s} 35ms/step - loss: 0.0491: end{sphinxVerbatim}

[1m421/422[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 35ms/step - loss: 0.0491

422/422 ━━━━━━━━━━━━━━━━━━━━ 19s 36ms/step - loss: 0.0491 - val_loss: 0.0342: </pre>
textbf{422/422} textcolor{ansi-green}{━━━━━━━━━━━━━━━━━━━━} textbf{19s} 36ms/step - loss: 0.0491 - val_loss: 0.0342: end{sphinxVerbatim}

[1m422/422[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 36ms/step - loss: 0.0491 - val_loss: 0.0342

Epoch 10/10

 1/422 ━━━━━━━━━━━━━━━━━━━━ 36:58 5s/step - loss: 0.0320: </pre>
textbf{ 1/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{36:58} 5s/step - loss: 0.0320: end{sphinxVerbatim}

[1m 1/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36:58[0m 5s/step - loss: 0.0320

 3/422 ━━━━━━━━━━━━━━━━━━━━ 16s 38ms/step - loss: 0.0375: </pre>
textbf{ 3/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 38ms/step - loss: 0.0375: end{sphinxVerbatim}

[1m 3/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 38ms/step - loss: 0.0375

 5/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.0399: </pre>
textbf{ 5/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0399: end{sphinxVerbatim}

[1m 5/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0399

 7/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.0404: </pre>
textbf{ 7/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0404: end{sphinxVerbatim}

[1m 7/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0404

 9/422 ━━━━━━━━━━━━━━━━━━━━ 16s 40ms/step - loss: 0.0401: </pre>
textbf{ 9/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 40ms/step - loss: 0.0401: end{sphinxVerbatim}

[1m 9/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 40ms/step - loss: 0.0401

 10/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0399: </pre>
textbf{ 10/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0399: end{sphinxVerbatim}

[1m 10/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0399

 12/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0392: </pre>
textbf{ 12/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0392: end{sphinxVerbatim}

[1m 12/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0392

 14/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0385: </pre>
textbf{ 14/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0385: end{sphinxVerbatim}

[1m 14/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0385

 16/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0384: </pre>
textbf{ 16/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0384: end{sphinxVerbatim}

[1m 16/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0384

 18/422 ━━━━━━━━━━━━━━━━━━━━ 17s 42ms/step - loss: 0.0393: </pre>
textbf{ 18/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{17s} 42ms/step - loss: 0.0393: end{sphinxVerbatim}

[1m 18/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 42ms/step - loss: 0.0393

 20/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0401: </pre>
textbf{ 20/422} textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0401: end{sphinxVerbatim}

[1m 20/422[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0401

 22/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0407: </pre>
textbf{ 22/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0407: end{sphinxVerbatim}

[1m 22/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0407

 24/422 ━━━━━━━━━━━━━━━━━━━━ 16s 42ms/step - loss: 0.0412: </pre>
textbf{ 24/422} textcolor{ansi-green}{━}textcolor{ansi-white}{━━━━━━━━━━━━━━━━━━━} textbf{16s} 42ms/step - loss: 0.0412: end{sphinxVerbatim}

[1m 24/422[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 42ms/step - loss: 0.0412

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[33]:

MultiDimensionalClassifier(
    model=<function get_model at 0x7f0aa2039c60>
    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