,

@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!