shapes are not aligned

[Louis-Fiacre]

Hi, I use your work in a learning goal. When I create small network with few layers everything works perfectly. But when i try this kind of network :
`reseau = Network()

reseau.add(FCLayer(8*8,100))
reseau.add(ActivationLayer(tanh, tanh_prime))
reseau.add(FCLayer(100,80))
reseau.add(ActivationLayer(tanh, tanh_prime))
reseau.add(FCLayer(80,60))
reseau.add(ActivationLayer(tanh, tanh_prime))
reseau.add(FCLayer(60,10))
reseau.add(ActivationLayer(tanh, tanh_prime))`

I have this issue when i use fit method :
`---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_367850/57939880.py in
1 reseau.use(mse, mse_prime)
----> 2 reseau.fit(learning_rate=0.0001, epochs=300, data_train=x_train, data_val=y_train)
3 reseau.show_fit()

~/Bureau/maths_python/14 avril/network.py in fit(self, learning_rate, epochs, data_train, data_val)
44
45 for layer in reversed(self.layers):
---> 46 error = layer.backward_propagation(error, learning_rate)
47
48 err /= iterations

~/Bureau/maths_python/14 avril/layer.py in backward_propagation(self, output_error, learning_rate)
49 def backward_propagation(self, output_error, learning_rate):
50 input_error = np.dot(output_error, self.weights.T)
---> 51 weights_error = np.dot(self.input.T, output_error)
52 # dBias = output_error
53

<array_function internals> in dot(*args, **kwargs)

ValueError: shapes (64,) and (1,100) not aligned: 64 (dim 0) != 1 (dim 0)`

When trying to debug it, I saw output_error changing type from ndarray to float64 but i really don't understand why.
Could you help me ? Thank in advance

[omaraflak]

You need to reshape your input as (64,1) before passing it in to the network. (64,) is considered different from (64,1).

[Louis-Fiacre]

Of course, thank a lot, i was trying to change it inside de network but not in the shape of my data... Another question, i have the same issue with the forward_propagation
`~/Bureau/maths_python/14 avril/layer.py in forward_propagation(self, input_data)
43 def forward_propagation(self, input_data):
44 self.input = input_data
---> 45 self.output = np.dot(self.input, self.weights) + self.bias
46 return self.output
47

<array_function internals> in dot(*args, **kwargs)

ValueError: shapes (64,1) and (64,100) not aligned: 1 (dim 1) != 64 (dim 0)

`
I'm missing something, do i need to transpose (64,1) another time ?

[omaraflak]

Sorry I thought you were commenting on another repo. Actually you need a row matrix.

So your input should be of size (1, 64).

What you pass to the fit method as x_train should be an array of such objects. Meaning, of shape (n, 1, 64). Likewise y_train should be (n, 1, 10).

[Louis-Fiacre]

Ok thank a lot. My data come from load_digits in sklearn_datasets, i will change ma dataset shape. I hope you will pass a good afternoon. Thank for your wonderful work :)