# GRADED FUNCTION: model

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)

    ### START CODE HERE ###

    # Step 1: CONV layer (≈4 lines)
    X = Conv1D(196, 15, strides=4)(X_input)             # CONV1D
#     X = BatchNormalization(axis=-1, momentum=0.99,epsilon=0.001,name="Batch")(X)                         # Batch normalization
    X = BatchNormalization(axis=-1, momentum=0.99,epsilon=0.001,name="Batch1")(X)                         # Batch normalization
    X = Activation('relu')(X)                           # ReLu activation
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 2: First GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences=True)(X)      # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    X = BatchNormalization()(X)                         # Batch normalization

    # Step 3: Second GRU Layer (≈4 lines)
    X = GRU(units = 128, return_sequences=True)(X)      # GRU (use 128 units and return the sequences)
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)
    X = BatchNormalization()(X)                         # Batch normalization
    X = Dropout(0.8)(X)                                 # dropout (use 0.8)

    # Step 4: Time-distributed dense layer (≈1 line)
    X = TimeDistributed(Dense(1, activation = "sigmoid"))(X) # time distributed  (sigmoid)

    ### END CODE HERE ###

    model = Model(inputs = X_input, outputs = X)

    return model