seq4.py

from keras.models import Model, load_model
from keras.layers import Input, Dense, Dropout
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras import regularizers

input_dim = X_train.shape[1] # the # features
encoding_dim = 8 # first layer
hidden_dim = int(encoding_dim / 2) #hideen layer

nb_epoch = 30
batch_size = 128
learning_rate = 0.1

input_layer = Input(shape=(input_dim, ))
encoder = Dense(encoding_dim, activation="tanh", activity_regularizer=regularizers.l1(10e-5))(input_layer)
encoder = Dense(hidden_dim, activation="relu")(encoder)
decoder = Dense(encoding_dim, activation='relu')(encoder)
decoder = Dense(input_dim, activation='tanh')(decoder)
autoencoder = Model(inputs=input_layer, outputs=decoder)

# ----- some data omitted --------- #

history = autoencoder.fit(X_train, X_train,
                    epochs=nb_epoch,
                    batch_size=batch_size,
                    shuffle=True,
                    validation_data=(X_test, X_test),
                    verbose=1,
                    callbacks=[checkpointer, tensorboard]).history