def convnet_cost(W_fb, b_fb, V, c, W, b, x, y1):
layer1 = tanh_conv_layer(W_fb, b_fb, x)
layer1_size = np.prod(layer1.shape[1:])
layer2 = tanh_layer(V, c,
np.reshape(layer1, (x.shape[0], layer1_size)))
cost = ova_svm_cost(W, b, layer2, y1)
return cost
def convnet_prediction(W_fb, b_fb, V, c, W, b, x):
layer1 = tanh_conv_layer(W_fb, b_fb, x)
layer1_size = np.prod(layer1.shape[1:])
layer2 = tanh_layer(V, c,
np.reshape(layer1, (x.shape[0], layer1_size)))
prediction = ova_svm_prediction(W, b, layer2)
return prediction
def convnet_cost(W_fb, b_fb, V, c, W, b, x, y1):
layer1 = tanh_conv_layer(W_fb, b_fb, x)
layer1_size = np.prod(layer1.shape[1:])
layer2 = tanh_layer(V, c, np.reshape(layer1, (x.shape[0], layer1_size)))
cost = ova_svm_cost(W, b, layer2, y1)
return cost
def convnet_prediction(W_fb, b_fb, V, c, W, b, x):
layer1 = tanh_conv_layer(W_fb, b_fb, x)
layer1_size = np.prod(layer1.shape[1:])
layer2 = tanh_layer(V, c, np.reshape(layer1, (x.shape[0], layer1_size)))
prediction = ova_svm_prediction(W, b, layer2)
return prediction
