LSTM.py

import matplotlib.pyplot as plt
from tensorflow.keras import datasets, layers, models

(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = models.Sequential([
    layers.LSTM(64, input_shape=(28, 28), activation='relu', return_sequences=True),
    layers.LSTM(128, activation='relu', return_sequences=True),
    layers.LSTM(256, activation='relu', return_sequences=True),
    layers.LSTM(512, activation='relu', return_sequences=True),
    layers.LSTM(256, activation='relu', return_sequences=True),
    layers.LSTM(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

model.summary()
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

history = model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test), batch_size=64)
test_loss, test_acc = model.evaluate(x_train, y_train, batch_size=64)