I have a CNN that needs to take in 68 images that are all 59x59 pixels. The CNN should output 136 values on the output layer
My training data has shape (-1, 68, 59, 59, 1).
My current approach is to use concatenate to join multiple networks like so:
input_layer = [None] * 68 x = [None] * 68 for i in range(68): input_layer[i] = tf.keras.layers.Input(shape=training_data.shape[1:][1:]) x[i] = Conv2D(64, (5,5))(input_layer[i]) x[i] = LeakyReLU(alpha=0.3)(x[i]) x[i] = MaxPooling2D(pool_size=(2,2))(x[i]) x[i] = Model(inputs=input_layer[i], outputs=x[i]) combined = concatenate(x)
However, this always gives the error:
ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs
Is this approach a suitable approach or am I doing this completely wrong?