def forward(self,
forward_final_output_value,
target_value,
is_train=True,
is_numba=False):
self.forward_final_output_value = forward_final_output_value
self.target_value = target_value
return tff.squared_error(forward_final_output_value,
target_value,
is_numba=is_numba)
def forward(self, forward_final_output_value, target_value):
self.forward_final_output_value = forward_final_output_value
self.target_value = target_value
return tff.squared_error(forward_final_output_value, target_value)
def forward(self, forward_final_output_value, target_value):
self.inputs = [forward_final_output_value, target_value]
return tff.squared_error(forward_final_output_value, target_value)
session = tfs.Session()
output = session.run(z, feed_dict={x: [1, 2]})
print(output)
# 20170912
# initialize a new graph
g.initialize()
# Create variables
w = tfg.Variable(5.0, name="w")
b = tfg.Variable(-1.0, name="b")
# Create placeholder
x = tfg.Placeholder(name="x")
# Create hidden node y
y = tfg.Affine(w, x, b, name="y")
session = tfs.Session()
output = session.run(y, feed_dict={x: 1.0})
squared_error = tff.squared_error(output, 14.0)
print("Output: {:4.1f}, Squared_Error: {:5.1f}".format(output, squared_error))
output = session.run(y, feed_dict={x: 2.0})
squared_error = tff.squared_error(output, 24.0)
print("Output: {:4.1f}, Squared_Error: {:5.1f}".format(output, squared_error))
output = session.run(y, feed_dict={x: 3.0})
squared_error = tff.squared_error(output, 34.0)
print("Output: {:4.1f}, Squared_Error: {:5.1f}".format(output, squared_error))