Myujth

I am trying to make a very simple functional neural network in Keras. I input a vector of shape (270000,) to the network, and have entered this as the shape to accept in the input layer, but I receive the error shown below. Given that the shape printed for the input specified to be at fault, is in fact (270000,), I don't know why I am receiving this error.

Model Function

def spectrify(A1, y1, simData, aOrigShape):
 print("A1: ", np.shape(A1))
 print("y1: ", np.shape(y1))
 print("simData", np.shape(simData))
 print("aOrigShape:", aOrigShape)
 dataIn = Input(shape=np.shape(A1))
 dataOut = Dense(np.shape(A1)[0])(dataIn)
 outShaper = Reshape((aOrigShape))(dataOut)
 model = Model(inputs = dataIn, outputs = outShaper)
 model.compile(optimizer = 'rmsprop',
 loss = 'categorical_crossentropy',
 metrics = ['accuracy'])
 model.fit(A1, simData)
 return model

Execution

Running the function above prints the shapes and raises the following error:

A1: (270000,)
y1: (200, 540)
simData (200, 400)
aOrigShape: (500, 540)

...
<ipython-input-130-88e6c1dfc1c9> in spectrify(A1, y1, simData, aOrigShape)
 12 loss = 'categorical_crossentropy',
 13 metrics = ['accuracy'])
---> 14 model.fit(A1, simData)
 15 return model
...

ValueError: Error when checking input: expected input_50 to have shape (270000,) but got array with shape (1,)

shape argument refers to the shape of one single sample in the training data. So if you have 270000 training samples of shape (1,), then the shape argument must be set to (1,). Otherwise, which is unlikely but possible, if you have one sample of shape (270000,) then you need the shape argument must be set to (270000,) and A must have a shape of (1, 270000), which means one sample of shape (270000,), and not (270000,) which means 270000 samples of shape (1,).

Generally, if X_train is the array which contains your training data, then it's a good practice to use X_train.shape[1:] (i.e. the shape of each sample) as the input shape, like this:

Input(shape=X_train.shape[1:])