machine_vision.py 文件源码

def create_training(self, image_size = [151,151]):
    """Create the cost function and trainer"""
    self.phi_input = tf.stop_gradient(tf.placeholder("float32", [None, image_size[0], image_size[1], 1]));
    def cost(output, phi_in):
       #return np.array([self.cost(o, phi_in) for o in output]);
      return np.sum(self.cost_func(output, phi_in));

    def cost_grad(op, grad):
      #print op
      output = op.inputs[0];
      phi = op.inputs[1];
      grad = tf.py_func(self.cost_func_grad, [output, phi], [tf.float32])[0];
      #return [self.cost_func_grad(output, phi_in, epsilon = 0.01), np.zeros((phi_in.shape))];
      return [grad, None];

    self.cost_tf = py_func(cost, [self.output, self.phi_input], [tf.float32], grad = cost_grad)[0];
    #self.cost_tf = tf.py_func(cost, [self.output, self.phi_input], [tf.float64])[0];
    #self.phi = tf.py_func(phi_func, [self.output], [tf.float64]);  
    #self.cost = tf.reduce_mean(tf.squared_difference(self.phi_input, self.phi));

    self.train_tf = tf.train.RMSPropOptimizer(0.00025,0.99,0.0,1e-6).minimize(self.cost_tf)