Error when trying to visualise Functional API model:- AttributeError: 'Functional' object has no attribute '_layers'

[fraseralex96]

Hi Team,

I receive the following error when I try and run my CNN model built using the Keras Functional API:

`--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Input In [18], in <cell line: 4>() 1 import visualkeras 2 from PIL import ImageFont ----> 4 visualkeras.graph_view(early_model)

File ~/.local/lib/python3.9/site-packages/visualkeras/graph.py:58, in graph_view(model, to_file, color_map, node_size, background_fill, padding, layer_spacing, node_spacing, connector_fill, connector_width, ellipsize_after, inout_as_tensor, show_neurons) 54 layer_y = list() 56 # Attach helper layers ---> 58 id_to_num_mapping, adj_matrix = model_to_adj_matrix(model) 59 model_layers = model_to_hierarchy_lists(model, id_to_num_mapping, adj_matrix) 61 # add fake output layers

File ~/.local/lib/python3.9/site-packages/visualkeras/layer_utils.py:39, in model_to_adj_matrix(model) 37 if not model.built: 38 model.build() ---> 39 layers = model._layers 41 adj_matrix = np.zeros((len(layers), len(layers))) 42 id_to_num_mapping = dict()

AttributeError: 'Functional' object has no attribute '_layers'`

Below you will find the function used to build my model:

`# CNN MODEL

def build_early_integration_CNN(X_train, learning_rate, momentum, seed):

# set layer weights initialiser
initializer = keras.initializers.GlorotUniform(seed=seed)

# drug-cell line data input
x_input = layers.Input(shape=(X_train.shape[1],1))
# 1st convolution layer
x = layers.Conv1D(filters=16, kernel_size=4, kernel_initializer=initializer, activation='relu')(x_input) 
x = layers.BatchNormalization()(x)
x = layers.MaxPooling1D()(x)
#x = layers.Dropout(0.1)(x)
# 2nd convolution layer
x = layers.Conv1D(filters=32, kernel_size=4, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling1D()(x)
#x = layers.Dropout(0.1)(x)
# 3rd convolution layer
x = layers.Conv1D(filters=64, kernel_size=4, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling1D()(x)
#x = layers.Dropout(0.1)(x)
# 4th convolution layer
x = layers.Conv1D(filters=64, kernel_size=4, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling1D()(x)
#x = layers.Dropout(0.1)(x)
x = layers.Flatten()(x)

# Concatenate phosphoproteomics and one-hot drug data + dense layers & activation layer
x = layers.Dense(256, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.Dense(128, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.Dense(64, kernel_initializer=initializer, activation='relu')(x)
x = layers.BatchNormalization()(x)
output = layers.Dense(1, kernel_initializer=initializer)(x) # actiation layer is 1 neuron for single regression prediction value

model = keras.Model(x_input, output)

model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=learning_rate, 
                                                    momentum=momentum), 
                                                    loss='mse', metrics=['mae'])
return model`

And the code used to call the function:

`#EARLY INTEGRATION

learning_rate = 1e-2 # initial learning rate, decays via learning rate scheduler momentum = 0.5 initializer_seed = 42

early_model = build_early_integration_CNN(X_train, learning_rate, momentum, initializer_seed)`

I would really appreciate your support as I am using these models for my MSc project and would love to include visualisations in my report!

Kind regards, Alex

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