In the Keras API, you can use the Input function to specify the shape of a list of arrays as an input to a model. The Input function takes a shape argument, which should be a tuple that specifies the shape of the input data, including the number of arrays and the size of each array.

Here's an example of how you might use the Input function to define a list of arrays as an input to a model in Keras:

from tensorflow.keras.layers import Input

# Define the input shape for a list of 3 arrays with 10 elements each
input_shape = (3, 10)
# Create an input layer for the model
inputs = Input(shape=input_shape)

You can then use the inputs variable as the first layer of your model, and it will accept a list of 3 arrays with 10 elements each as input.

For example, you might use this input layer as the first layer of a model that processes the arrays and outputs a single prediction:

from tensorflow.keras.layers import Dense

# Add additional layers to the model
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)

# Create the model using the input layer and the output predictions
model = Model(inputs=inputs, outputs=predictions)

You can then compile and fit the model using a list of arrays as input data, using the fit method of the model:

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Define the input data as a list of arrays
input_data = [array1, array2, array3]

# Define the labels for the input data
labels = [0, 1, 1]

# Fit the model to the input data and labels
model.fit(input_data, labels, epochs=10, batch_size=32)

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