tr, vl = extractData()
tr, vl = extractData()
weight = {
'W':
np.expand_dims(np.expand_dims(np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]]),
axis=-1),
axis=-1),
'B':
np.ones((1, 1))
}
batch = tr[0][:10, :, :]
cnn = CNN.ConvNN()
result = cnn.convolution_layer(batch, weight)
for i in range(result.shape[0]):
myimshow(result[i, :, :, 0])
#plt.show()
b_res = result[:, :, :, :]
back = cnn.convolution_layer_backward(b_res, weight, batch)
out, mask = cnn.max_pooling(batch)
res = cnn.max_pooling_backward(out, mask)
