CODEWITHSUNDEEP
pythontensorflowinputneural-network
Qilos
Qilos

Reputation: 67

Multiple Inputs for neural network in tensorflow?

Im trying to implement a reinforcement learning algorithm with tensorflow to train an agent.

I want my neural network to have 2 different inputs, the first one an image stack of 4 images with the shape (4,160,120,1) and then just a one dimensional array with 10 entries.

I tried to do it like i did with just one input and defined my call function of my neural network with two inputs and ran my program. When the function train_on_batch was executed it resulted in an error and i received following message, in which states2 is my second input:

ValueError: Models passed to train_on_batch can only have training and the first argument in call as positional arguments, found: ['state2']

So how can I use two inputs for my neural network and still be able to use train_on_batch?

Upvotes: 1

Views: 2006

Answers (2)

Gerry P
Gerry P

Reputation: 8092

You can create a model with two inputs something like

input1=tf.keras.Input( shape= .....
# add layers here to process input 1 as you wish 
# last layer should be a Flatten layer or GlobalMaxPooling Layer
out1=tf.keras.layers.Flatten()(previous layer)
input2= tf.keras.Input ( shape=....
add layers  to process input 2
# last layer should be a Flatten layer or GlobalMaxPooling Layer
out2=tf.keras.layers.Flatten()(previous layer)
# now concatenate the outputs out1 and out2
concatted = tf.keras.layers.Concatenate()([out1, out2])
# now you can add more layers here to process the concatted output as you wish
#  last layer should be your output layer
output=Dense (number of classes, activation='softmax;)(previous layer output)
model=keras.Model(inputs=[input1,input2], outputs=output)
#  then compile your model

Upvotes: 1

DJ001
DJ001

Reputation: 75

You would need to either concatenate the inputs into a single npy array, or use a list of arrays, as stated in the documentation when running the tf.keras.Model.train_on_batch() function.

Upvotes: 0

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