doc/master/GenerateModel_8py.html

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Activation

from tensorflow.keras.regularizers import l2

from tensorflow.keras.optimizers import SGD

from tensorflow.keras.utils import plot_model


# Setup the model here

num_input_nodes = 4

num_output_nodes = 2

num_hidden_layers = 1

nodes_hidden_layer = 64

l2_val = 1e-5


model = Sequential()


# Hidden layer 1

# NOTE: Number of input nodes need to be defined in this layer

model.add(Dense(nodes_hidden_layer, activation='relu', kernel_regularizer=l2(l2_val), input_dim=num_input_nodes))


# Hidden layer 2 to num_hidden_layers

# NOTE: Here, you can do what you want

for k in range(num_hidden_layers-1):

    model.add(Dense(nodes_hidden_layer, activation='relu', kernel_regularizer=l2(l2_val)))


# Output layer

# NOTE: Use following output types for the different tasks

# Binary classification: 2 output nodes with 'softmax' activation

# Regression: 1 output with any activation ('linear' recommended)

# Multiclass classification: (number of classes) output nodes with 'softmax' activation

model.add(Dense(num_output_nodes, activation='softmax'))


# Compile model

# NOTE: Use following settings for the different tasks

# Any classification: 'categorical_crossentropy' is recommended loss function

# Regression: 'mean_squared_error' is recommended loss function

model.compile(loss='categorical_crossentropy', optimizer=SGD(learning_rate=0.01), weighted_metrics=['accuracy',])


# Save model

model.save('model.h5')


# Additional information about the model

# NOTE: This is not needed to run the model


# Print summary

model.summary()


# Visualize model as graph

try:

    plot_model(model, to_file='model.png', show_shapes=True)

except:

    print('[INFO] Failed to make model plot')