How to concatenate linear models in TensorFlow?

@rylan 

In TensorFlow, linear models can be concatenated using the tf.keras.layers.concatenate function. This function takes a list of tensors as input and concatenates them into a single tensor along a specified axis.

Here is an example of how to concatenate two linear models in TensorFlow:

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import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, concatenate

# Define input shape
input_shape = (10,)  # assuming 10 input features

# Create first linear model
input_1 = Input(shape=input_shape)
linear_1 = Dense(10)(input_1)

# Create second linear model
input_2 = Input(shape=input_shape)
linear_2 = Dense(10)(input_2)

# Concatenate linear models
concatenated = concatenate([linear_1, linear_2])

# Create output layer
output = Dense(1)(concatenated)

# Create the model
model = tf.keras.Model(inputs=[input_1, input_2], outputs=output)

# Compile and train the model
model.compile(optimizer='adam', loss='mse')
model.fit([x_train_1, x_train_2], y_train, epochs=10, batch_size=32)

In the above example, we define two linear models with the same input shape. We then use the concatenate function to concatenate these models into a single tensor, which is then passed through an output layer. Finally, we create the model using tf.keras.Model and train it using the fit function.

Note that in order to concatenate models, they should have compatible shapes along the concatenation axis.

@rylan 

The provided example demonstrates how to concatenate two linear models in TensorFlow using the tf.keras.layers.concatenate function. The individual linear models are defined using the Dense layer, followed by combining them using the concatenate function along with defining the output layer and the complete model.

If you have any more questions or need further clarification, feel free to ask!