Precipitation prediction model

preciptiation_prediction_model.py

def create_model():
    model = Sequential()
    model.add(ConvLSTM2D(filters=64, kernel_size=(7, 7),
                    input_shape=(18,344,315,1),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(BatchNormalization())
    model.add(ConvLSTM2D(filters=64, kernel_size=(5, 5),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(BatchNormalization())
    model.add(ConvLSTM2D(filters=64, kernel_size=(3, 3),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(BatchNormalization())
    model.add(ConvLSTM2D(filters=64, kernel_size=(1, 1),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
                activation='sigmoid',
                padding='same', data_format='channels_last'))
    return model

model = create_model()

model.compile(loss='binary_crossentropy', optimizer='adadelta')
keras.utils.plot_model(model, to_file="model.png", show_dtype=True, show_layer_activations=True, show_shapes=True)
print(model.summary())

epochs = 25
batch_size = 1

#Fit the model
model.fit(
    X_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    validation_data=(X_val, y_val),
    verbose=1,
)