def model(input_shape):
"""
Function creating the model's graph in Keras.
Argument:
input_shape -- shape of the model's input data (using Keras conventions)
Returns:
model -- Keras model instance
"""
X_input = Input(shape=input_shape)
conv_1 = Conv3D(filters=32,
kernel_size=(1, 1, 1),
padding="same",
strides=(1, 1, 1),
activation="elu")(X_input)
conv_2 = Conv3D(filters=64,
kernel_size=(1, 1, 1),
padding="same",
strides=(1, 1, 1),
activation="elu")(conv_1)
conv_3 = Conv3D(filters=128,
kernel_size=(1, 1, 1),
padding="same",
strides=(1, 1, 1),
activation="elu")(conv_2)
shape = conv_3.get_shape().as_list()
pool_2_flat = Reshape([shape[1], shape[2] * shape[3] * shape[4]])(conv_3)
fc = Dense(1024, activation="elu")(pool_2_flat)
fc_drop = Dropout(dropout_prob)(fc)
lstm_in = Reshape([10, 1024])(fc_drop)
# lstm_1 = LSTM(units=1024, return_sequences=True, unit_forget_bias=True, dropout=dropout_prob)(lstm_in)
# rnn_output = LSTM(units=1024, return_sequences=False, unit_forget_bias=True)(lstm_1)
lstm_1 = GRU(units=1024, return_sequences=True,
dropout=dropout_prob)(lstm_in)
rnn_output = GRU(units=1024, return_sequences=False)(lstm_1)
shape_rnn_out = rnn_output.get_shape().as_list()
fc_out = Dense(shape_rnn_out[1], activation="elu")(rnn_output)
fc_drop = Dropout(dropout_prob)(fc_out)
y_ = Dense(n_labels)(fc_drop)
y_posi = Softmax()(y_)
model = Model(inputs=X_input, outputs=y_posi)
return model
