custom neural network in python

main.py
import tensorflow as tf
from tensorflow import keras
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main.py
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
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main.py
X_train = X_train.reshape((-1, 28 * 28))
X_train = X_train.astype('float32') / 255

X_test = X_test.reshape((-1, 28 * 28))
X_test = X_test.astype('float32') / 255

y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)
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main.py
model = keras.Sequential([
    keras.layers.Dense(256, activation='relu', input_shape=(784,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])
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main.py
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
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main.py
model.fit(X_train, y_train,
          epochs=20,
          batch_size=64,
          validation_data=(X_test, y_test))
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main.py
test_loss, test_acc = model.evaluate(X_test, y_test)
print(f'Test accuracy: {test_acc}')
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