s_autoencoder.py

#Create the train and test set

TRAIN_RATIO = 0.75

train_size = int(len(vec_seqs) * TRAIN_RATIO)
X_train = vec_seqs[:train_size]
X_test = vec_seqs[train_size:]

#define the encoder

input_dim = X_train.shape[1] #features num
encoding_dim = 32 #hidden layer size

nb_epoch = 3
batch_size = 128
learning_rate = 1e-2

input_layer = Input(shape=(input_dim,))

encoder = Dense(encoding_dim, activation="relu", activity_regularizer=regularizers.l1(learning_rate))(input_layer)

decoder = Dense(input_dim, activation="relu")(encoder)

autoencoder = Model(inputs=input_layer, outputs=decoder)

autoencoder.summary()