Hi,
You can use the functional API to define the two models and specify the inputs and outputs.
Here is an example code snippet:
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense
# Define the first model
inputs1 = Input(shape=(input_shape,))
x1 = Dense(units=hidden_units, activation='relu')(inputs1)
outputs1 = Dense(units=output_units, activation='softmax')(x1)
model1 = keras.Model(inputs=inputs1, outputs=outputs1)
# Define the second model
inputs2 = Input(shape=(input_shape,))
x2 = Dense(units=hidden_units, activation='relu')(inputs2)
outputs2 = Dense(units=output_units, activation='softmax')(x2)
model2 = keras.Model(inputs=inputs2, outputs=outputs2)
# Connect the two models
x1 = model1(inputs2)
outputs = model2(x1)
3rd_model = keras.Model(inputs=inputs2, outputs=outputs)
# Compile the models
model1.compile(optimizer='adam', loss='categorical_crossentropy')
model2.compile(optimizer='adam', loss='categorical_crossentropy')
3rd_model.compile(optimizer='adam', loss='categorical_crossentropy')
# Train the models
model1.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))
model2.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))
3rd_model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))
# Evaluate the second model only
model2.evaluate(x_test, y_test)
We first define the two models (model1
and model2
) separately. We then connect the two models by passing the output of the first model (x1
) as input to the second model, and then create a third model (3rd_model
) that combines both models and takes the same input as the second model.
We compile all three models with the same optimizer and loss function, and train them on the training data. Finally, to evaluate only the second model, we use the evaluate()
method on the model2
object and pass in the test data.
Note that because the gradients of the second model do not pass through to the first model during training, we can train each model separately and then combine them for testing.
Please let me know if it helps you to resolve your problem.