TensorFlow 入门 DIGITS

            

            
class UserModel(Tower):

    @model_propertyOther TensorFlow Tools in DIGITS
    def inference(self):
        # Your code here
        return model

    @model_property#with tf.variable_scope(digits.GraphKeys.MODEL, reuse=None):
    def loss(self):
        # Your code here
        return loss
        

            

            
class UserModel(Tower):

    @model_property
    def inference(self):
        x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
        # scale (divide by MNIST std)
        x = x * 0.0125
        with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            weights_initializer=tf.contrib.layers.xavier_initializer(),
                            weights_regularizer=slim.l2_regularizer(0.0005) ):
            model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='conv1')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
            model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
            model = slim.flatten(model)
            model = slim.fully_connected(model, 500, scope='fc1')
            model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
            model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc2')
            return model

    @model_property
    def loss(self):
        loss = digits.classification_loss(self.inference, self.y)
        accuracy = digits.classification_accuracy(self.inference, self.y)
        self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
        return loss
        

            

            
$ tensorboard --logdir <job_dir>/tb/
        

            

            
class UserModel(Tower):

    @model_property
    def inference(self):

        # the order for input shape is [0] -> H, [1] -> W, [2] -> C
        # this is because tensorflow's default order is NHWC
        model = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
        image_dim = self.input_shape[0] * self.input_shape[1]

        with slim.arg_scope([slim.fully_connected], 
                        weights_initializer=tf.contrib.layers.xavier_initializer(),
                        weights_regularizer=slim.l2_regularizer(0.0005)):

            # first we reshape the images to something
            model = tf.reshape(_x, shape=[-1, image_dim])

            # encode the image
            model = slim.fully_connected(model, 300, scope='fc1')
            model = slim.fully_connected(model, 50, scope='fc2')

            # decode the image
            model = slim.fully_connected(model, 300, scope='fc3')
            model = slim.fully_connected(model, image_dim, activation_fn=None, scope='fc4')

            # form it back to the original
            model = tf.reshape(model, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
            
            return model

    @model_property
    def loss(self):

        # In an autoencoder, we compare the encoded and then decoded image with the original
        original = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])

        # self.inference is called to get the processed image
        model = self.inference
        loss = digits.mse_loss(original, model)

        return loss
        

            

            
class UserModel(Tower):

    @model_property
    def inference(self):

        model = construct_model()
        """code to construct the network omitted"""

        # assuming the original model have weight2 and bias2 variables
        # in here, we renamed them by adding the suffix _not_in_use
        # this tells TensorFlow that these variables in the pre-trained model should
        # not be retrained and it should be frozen
        # If we would like to freeze a weight, all we have to do is just rename it
        self.weights = {
            'weight1': tf.get_variable('weight1', [5, 5, self.input_shape[2], 20], initializer=tf.contrib.layers.xavier_initializer()),
            'weight2': tf.get_variable('weight2_not_in_use', [5, 5, 20, 50], initializer=tf.contrib.layers.xavier_initializer())
        }

        self.biases = {
            'bias1': tf.get_variable('bias1', [20], initializer=tf.constant_initializer(0.0)),
            'bias2': tf.get_variable('bias2_not_in_use', [50], initializer=tf.constant_initializer(0.0))
        }

        return model

    @model_property
    def loss(self):
        loss = calculate_loss()
        """code to calculate loss omitted"""
        return loss