def evaluate(self, flag, x, y):
'''Compuate the averaged error.
Returns:
a float value as the averaged error
'''
return tensor.sum(tensor.square(x - y) * 0.5) / x.size()
def evaluate(self, flag, x, y):
'''Compuate the averaged error.
Returns:
a float value as the averaged error
'''
return tensor.sum(tensor.square(x - y) * 0.5) / x.size()
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
def add_hide_layer(inputs, in_size, out_size, activation_function=None):
Weights = tensor.Variable(np.zeros([in_size, out_size]))
Wx = Weights * inputs
if activation_function is None:
outputs = Wx
else:
outputs = activation_function(Wx)
return outputs
l1 = add_input_layer(x, 1, 10, activation_function=tensor.relu)
# l1 = add_input_layer(x, 1, 10)
# l1.derivative()
# add output layer
y = add_hide_layer(l1, 10, 1, activation_function=None)
loss = tensor.sum((y - tf_y)**2)
train = tensor.minimize(loss)
for step in range(100):
print('loss = ', tensor.run(train, {tf_x: x, tf_y: y}))