python类multiply()的实例源码

def SampleRandomFrames(model_input, num_frames, num_samples):
  """Samples a random set of frames of size num_samples.

  Args:
    model_input: A tensor of size batch_size x max_frames x feature_size
    num_frames: A tensor of size batch_size x 1
    num_samples: A scalar

  Returns:
    `model_input`: A tensor of size batch_size x num_samples x feature_size
  """
  batch_size = tf.shape(model_input)[0]
  frame_index = tf.cast(
      tf.multiply(
          tf.random_uniform([batch_size, num_samples]),
          tf.tile(tf.cast(num_frames, tf.float32), [1, num_samples])), tf.int32)
  batch_index = tf.tile(
      tf.expand_dims(tf.range(batch_size), 1), [1, num_samples])
  index = tf.stack([batch_index, frame_index], 2)
  return tf.gather_nd(model_input, index)

def _build_net(self, input_BO, scope):
        """ The Actor network.

        Uses ReLUs for all hidden layers, but a tanh to the output to bound the
        action. This follows their 'low-dimensional networks' using 400 and 300
        units for the hidden layers. Set `reuse=False`. I don't use batch
        normalization or their precise weight initialization.
        """
        with tf.variable_scope(scope, reuse=False):
            hidden1 = layers.fully_connected(input_BO,
                    num_outputs=400,
                    weights_initializer=layers.xavier_initializer(),
                    activation_fn=tf.nn.relu)
            hidden2 = layers.fully_connected(hidden1, 
                    num_outputs=300,
                    weights_initializer=layers.xavier_initializer(),
                    activation_fn=tf.nn.relu)
            actions_BA = layers.fully_connected(hidden2,
                    num_outputs=self.ac_dim,
                    weights_initializer=layers.xavier_initializer(),
                    activation_fn=tf.nn.tanh) # Note the tanh!
            # This should broadcast, but haven't tested with ac_dim > 1.
            actions_BA = tf.multiply(actions_BA, self.ac_high)
            return actions_BA

def yuv2rgb(yuv):
    """
    Convert YUV image into RGB https://en.wikipedia.org/wiki/YUV
    """
    yuv = tf.multiply(yuv, 255)
    yuv2rgb_filter = tf.constant([[[[1., 1., 1.], [0., -0.34413999, 1.77199996],
                                    [1.40199995, -0.71414, 0.]]]])
    yuv2rgb_bias = tf.constant([-179.45599365, 135.45983887, -226.81599426])

    yuv = tf.expand_dims(yuv, 0)
    temp = tf.nn.conv2d(yuv, yuv2rgb_filter, [1, 1, 1, 1], 'SAME')
    temp = tf.nn.bias_add(temp, yuv2rgb_bias)
    temp = tf.maximum(temp, tf.zeros(temp.get_shape(), dtype=tf.float32))
    temp = tf.minimum(temp,
                      tf.multiply(
                          tf.ones(temp.get_shape(), dtype=tf.float32), 255))
    temp = tf.divide(temp, 255)
    temp = tf.squeeze(temp, [0])
    return temp

def align(hid_align, h_dec, scope):
    h_dec_align = linear3(h_dec, dim_align, "h_dec_align_"+scope) #batch_size x dimAlign
    h_dec_align = tf.reshape(h_dec_align,[batch_size,1,dim_align])
    h_dec_align_tiled = tf.tile(h_dec_align, [1, sentence_length, 1])
    all_align = tf.tanh(h_dec_align + hid_align) 

    with tf.variable_scope("v_align_"+scope, reuse = DO_SHARE):
        v_align=tf.get_variable("v_align_"+scope, [dim_align], initializer=tf.constant_initializer(0.0))
    e_t = all_align * v_align
    e_t = tf.reduce_sum(e_t, 2)

    # normalise
    alpha = tf.nn.softmax(e_t) # batch_size x sentence_length
    alpha_t = tf.reshape(alpha, [batch_size, sentence_length, 1])
    alpha_tile = tf.tile(alpha_t, [1, 1, 2*y_enc_size])
    s_t = tf.multiply(alpha_tile, h_t_lang)

    s_t = tf.reduce_sum(s_t, 1)

    return s_t,alpha

def cal_loss(self):
        one_hot_labels = tf.one_hot(
            self.labels, depth=self.conf.class_num,
            axis=self.channel_axis, name='labels/one_hot')
        losses = tf.losses.softmax_cross_entropy(
            one_hot_labels, self.predictions, scope='loss/losses')
        self.loss_op = tf.reduce_mean(losses, name='loss/loss_op')
        self.decoded_preds = tf.argmax(
            self.predictions, self.channel_axis, name='accuracy/decode_pred')
        correct_prediction = tf.equal(
            self.labels, self.decoded_preds,
            name='accuracy/correct_pred')
        self.accuracy_op = tf.reduce_mean(
            tf.cast(correct_prediction, tf.float32, name='accuracy/cast'),
            name='accuracy/accuracy_op')
        # weights = tf.cast(
        #     tf.greater(self.decoded_preds, 0, name='m_iou/greater'),
        #     tf.int32, name='m_iou/weights')
        weights = tf.cast(
            tf.less(self.labels, self.conf.channel, name='m_iou/greater'),
            tf.int64, name='m_iou/weights')
        labels = tf.multiply(self.labels, weights, name='m_iou/mul')
        self.m_iou, self.miou_op = tf.metrics.mean_iou(
            self.labels, self.decoded_preds, self.conf.class_num,
            weights, name='m_iou/m_ious')

def build_model(user_indices, item_indices, rank, ratings, user_cnt, item_cnt, lr, lamb, mu, init_value):


    W_user = tf.Variable(tf.truncated_normal([user_cnt, rank], stddev=init_value/math.sqrt(float(rank)), mean=0), name = 'user_embedding', dtype=tf.float32)
    W_item = tf.Variable(tf.truncated_normal([item_cnt, rank], stddev=init_value/math.sqrt(float(rank)), mean=0), name = 'item_embedding', dtype=tf.float32)

    W_user_bias = tf.concat([W_user, tf.ones((user_cnt,1), dtype=tf.float32)], 1, name='user_embedding_bias')
    W_item_bias = tf.concat([tf.ones((item_cnt,1), dtype=tf.float32), W_item], 1, name='item_embedding_bias')

    user_feature = tf.nn.embedding_lookup(W_user_bias, user_indices, name = 'user_feature')
    item_feature = tf.nn.embedding_lookup(W_item_bias, item_indices, name = 'item_feature') 


    preds = tf.add(tf.reduce_sum( tf.multiply(user_feature , item_feature) , 1), mu)

    square_error = tf.sqrt(tf.reduce_mean( tf.squared_difference(preds, ratings)))
    loss = square_error + lamb*(tf.reduce_mean(tf.nn.l2_loss(W_user)) + tf.reduce_mean(tf.nn.l2_loss(W_item)))

    tf.summary.scalar('square_error', square_error)
    tf.summary.scalar('loss', loss)
    merged_summary = tf.summary.merge_all()
    #tf.global_variables_initializer()
    train_step = tf.train.GradientDescentOptimizer(lr).minimize(loss)   # tf.train.AdadeltaOptimizer(learning_rate=lr).minimize(loss)    #

    return train_step, square_error, loss, merged_summary

def _meshgrid(self):
        with tf.variable_scope('_meshgrid'):
            x_t = tf.matmul(tf.ones(shape=tf.stack([self.out_height, 1])),
                            tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, self.out_width), 1), [1, 0]))
            y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, self.out_height), 1),
                            tf.ones(shape=tf.stack([1, self.out_width])))
            x_t_flat = tf.reshape(x_t, (1, -1))
            y_t_flat = tf.reshape(y_t, (1, -1))
            px,py = tf.stack([x_t_flat],axis=2),tf.stack([y_t_flat],axis=2)
            #source control points
            x,y = tf.linspace(-1.,1.,self.Column_controlP_number),tf.linspace(-1.,1.,self.Row_controlP_number)
            x,y = tf.meshgrid(x,y)
            xs,ys = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1)))
            cpx,cpy = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2])
            px, cpx = tf.meshgrid(px,cpx);py, cpy = tf.meshgrid(py,cpy)           
            #Compute distance R
            Rx,Ry = tf.square(tf.subtract(px,cpx)),tf.square(tf.subtract(py,cpy))
            R = tf.add(Rx,Ry)          
            R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10)))
            #Source coordinates
            ones = tf.ones_like(x_t_flat) 
            grid = tf.concat([ones, x_t_flat, y_t_flat,R],0)
            grid = tf.reshape(grid,[-1])
            grid = tf.reshape(grid,[self.Column_controlP_number*self.Row_controlP_number+3,self.out_height*self.out_width])
            return grid

def _variable_with_weight_decay(name, shape, stddev, wd):
  """Helper to create an initialized Variable with weight decay.

  Note that the Variable is initialized with a truncated normal distribution.
  A weight decay is added only if one is specified.

  Args:
    name: name of the variable
    shape: list of ints
    stddev: standard deviation of a truncated Gaussian
    wd: add L2Loss weight decay multiplied by this float. If None, weight
        decay is not added for this Variable.

  Returns:
    Variable Tensor
  """
  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
  var = _variable_on_cpu(
      name,
      shape,
      tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
  if wd is not None:
    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
    tf.add_to_collection('losses', weight_decay)
  return var

def L2(tensor, wd=0.001):
    """ L2.

    Computes half the L2 norm of a tensor without the `sqrt`:

      output = sum(t ** 2) / 2 * wd

    Arguments:
        tensor: `Tensor`. The tensor to apply regularization.
        wd: `float`. The decay.

    Returns:
        The regularization `Tensor`.

    """
    return tf.multiply(tf.nn.l2_loss(tensor), wd, name='L2-Loss')

def L1(tensor, wd=0.001):
    """ L1.

    Computes the L1 norm of a tensor:

      output = sum(|t|) * wd

    Arguments:
        tensor: `Tensor`. The tensor to apply regularization.
        wd: `float`. The decay.

    Returns:
        The regularization `Tensor`.

    """
    return tf.multiply(tf.reduce_sum(tf.abs(tensor)), wd, name='L1-Loss')

def average_gradients(tower_grads):
    """Calculate the average gradient for each shared variable across all towers.

    Note that this function provides a synchronization point across all towers.

    Args:
      tower_grads: List of lists of (gradient, variable) tuples. The outer list
        is over individual gradients. The inner list is over the gradient
        calculation for each tower.
    Returns:
       List of pairs of (gradient, variable) where the gradient has been averaged
       across all towers.
    """
    average_grads = []
    for single_grads in zip(*tower_grads):
        grads = [g for g, _ in single_grads]
        grad = tf.add_n(grads)
        grad = tf.multiply(grad, 1.0/len(grads))
        v = single_grads[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def average_gradients(tower_grads):
    """Calculate the average gradient for each shared variable across all towers.

    Note that this function provides a synchronization point across all towers.

    Args:
      tower_grads: List of lists of (gradient, variable) tuples. The outer list
        is over individual gradients. The inner list is over the gradient
        calculation for each tower.
    Returns:
       List of pairs of (gradient, variable) where the gradient has been averaged
       across all towers.
    """
    average_grads = []
    for single_grads in zip(*tower_grads):
        grads = [g for g, _ in single_grads]
        grad = tf.add_n(grads)
        grad = tf.multiply(grad, 1.0/len(grads))
        v = single_grads[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def average_gradients(tower_grads):
    """Calculate the average gradient for each shared variable across all towers.

    Note that this function provides a synchronization point across all towers.

    Args:
      tower_grads: List of lists of (gradient, variable) tuples. The outer list
        is over individual gradients. The inner list is over the gradient
        calculation for each tower.
    Returns:
       List of pairs of (gradient, variable) where the gradient has been averaged
       across all towers.
    """
    average_grads = []
    for single_grads in zip(*tower_grads):
        grads = [g for g, _ in single_grads]
        grad = tf.add_n(grads)
        grad = tf.multiply(grad, 1.0/len(grads))
        v = single_grads[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def SampleRandomFrames(model_input, num_frames, num_samples):
  """Samples a random set of frames of size num_samples.

  Args:
    model_input: A tensor of size batch_size x max_frames x feature_size
    num_frames: A tensor of size batch_size x 1
    num_samples: A scalar

  Returns:
    `model_input`: A tensor of size batch_size x num_samples x feature_size
  """
  batch_size = tf.shape(model_input)[0]
  frame_index = tf.cast(
      tf.multiply(
          tf.random_uniform([batch_size, num_samples]),
          tf.tile(tf.cast(num_frames, tf.float32), [1, num_samples])), tf.int32)
  batch_index = tf.tile(
      tf.expand_dims(tf.range(batch_size), 1), [1, num_samples])
  index = tf.stack([batch_index, frame_index], 2)
  return tf.gather_nd(model_input, index)

def image_preprocessing(image, train):
    """Decode and preprocess one image for evaluation or training.

    Args:
      image: JPEG
      train: boolean
    Returns:
      3-D float Tensor containing an appropriately scaled image

    Raises:
       ValueError: if user does not provide bounding box
    """
    with tf.name_scope('image_preprocessing'):
        if train:
            image = tf.image.random_flip_left_right(image)
            image = tf.image.random_brightness(image, 0.6)
            if FLAGS.image_channel >= 3:
                image = tf.image.random_saturation(image, 0.6, 1.4)
        # Finally, rescale to [-1,1] instead of [0, 1)
        image = tf.subtract(image, 0.5)
        image = tf.multiply(image, 2.0)
        image = tf.image.per_image_standardization(image)
        return image

def testComposedMaps(self):
    def preprocessing_fn(inputs):
      return {
          'a(b+c)': tf.multiply(
              inputs['a'], tf.add(inputs['b'], inputs['c']))
      }

    input_data = [{'a': 4, 'b': 3, 'c': 3}, {'a': 1, 'b': 2, 'c': 1}]
    input_metadata = dataset_metadata.DatasetMetadata({
        'a': sch.ColumnSchema(tf.float32, [], sch.FixedColumnRepresentation()),
        'b': sch.ColumnSchema(tf.float32, [], sch.FixedColumnRepresentation()),
        'c': sch.ColumnSchema(tf.float32, [], sch.FixedColumnRepresentation())
    })
    expected_data = [{'a(b+c)': 24}, {'a(b+c)': 3}]
    expected_metadata = dataset_metadata.DatasetMetadata({
        'a(b+c)': sch.ColumnSchema(
            tf.float32, [], sch.FixedColumnRepresentation())
    })
    self.assertAnalyzeAndTransformResults(
        input_data, input_metadata, preprocessing_fn, expected_data,
        expected_metadata)

def should_distort_images(flip_left_right, random_crop, random_scale,
                          random_brightness):
  """Whether any distortions are enabled, from the input flags.

  Args:
    flip_left_right: Boolean whether to randomly mirror images horizontally.
    random_crop: Integer percentage setting the total margin used around the
    crop box.
    random_scale: Integer percentage of how much to vary the scale by.
    random_brightness: Integer range to randomly multiply the pixel values by.

  Returns:
    Boolean value indicating whether any distortions should be applied.
  """
  return (flip_left_right or (random_crop != 0) or (random_scale != 0) or
          (random_brightness != 0))

def preprocess_for_eval(image, height, width,
                        central_fraction=0.875, scope=None):
  """Prepare one image for evaluation.
  If height and width are specified it would output an image with that size by
  applying resize_bilinear.
  If central_fraction is specified it would cropt the central fraction of the
  input image.
  Args:
    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
      [0, 1], otherwise it would converted to tf.float32 assuming that the range
      is [0, MAX], where MAX is largest positive representable number for
      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details)
    height: integer
    width: integer
    central_fraction: Optional Float, fraction of the image to crop.
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor of prepared image.
  """
  with tf.name_scope(scope, 'eval_image', [image, height, width]):
    if image.dtype != tf.float32:
      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    # Crop the central region of the image with an area containing 87.5% of
    # the original image.
    if central_fraction:
      image = tf.image.central_crop(image, central_fraction=central_fraction)

    if height and width:
      # Resize the image to the specified height and width.
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(image, [height, width],
                                       align_corners=False)
      image = tf.squeeze(image, [0])
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    return image

def noisy_dense(inputs, units, bias_shape, c_names, w_i, b_i=None, activation=tf.nn.relu, noisy_distribution='factorised'):
    def f(e_list):
        return tf.multiply(tf.sign(e_list), tf.pow(tf.abs(e_list), 0.5))
    # ??tf.layers?????flatten
    # dense1 = tf.layers.dense(tf.contrib.layers.flatten(relu5), activation=tf.nn.relu, units=50)
    if not isinstance(inputs, ops.Tensor):
        inputs = ops.convert_to_tensor(inputs, dtype='float')
        # dim_list = inputs.get_shape().as_list()
        # flatten_shape = dim_list[1] if len(dim_list) <= 2 else reduce(lambda x, y: x * y, dim_list[1:])
        # reshaped = tf.reshape(inputs, [dim_list[0], flatten_shape])
    if len(inputs.shape) > 2:
        inputs = tf.contrib.layers.flatten(inputs)
    flatten_shape = inputs.shape[1]
    weights = tf.get_variable('weights', shape=[flatten_shape, units], initializer=w_i)
    w_noise = tf.get_variable('w_noise', [flatten_shape, units], initializer=w_i, collections=c_names)
    if noisy_distribution == 'independent':
        weights += tf.multiply(tf.random_normal(shape=w_noise.shape), w_noise)
    elif noisy_distribution == 'factorised':
        noise_1 = f(tf.random_normal(tf.TensorShape([flatten_shape, 1]), dtype=tf.float32))  # ???????????????
        noise_2 = f(tf.random_normal(tf.TensorShape([1, units]), dtype=tf.float32))
        weights += tf.multiply(noise_1 * noise_2, w_noise)
    dense = tf.matmul(inputs, weights)
    if bias_shape is not None:
        assert bias_shape[0] == units
        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
        b_noise = tf.get_variable('b_noise', [1, units], initializer=b_i, collections=c_names)
        if noisy_distribution == 'independent':
            biases += tf.multiply(tf.random_normal(shape=b_noise.shape), b_noise)
        elif noisy_distribution == 'factorised':
            biases += tf.multiply(noise_2, b_noise)
        return activation(dense + biases) if activation is not None else dense + biases
    return activation(dense) if activation is not None else dense


# ???bias??????relu

def SampleRandomSequence(model_input, num_frames, num_samples):
  """Samples a random sequence of frames of size num_samples.

  Args:
    model_input: A tensor of size batch_size x max_frames x feature_size
    num_frames: A tensor of size batch_size x 1
    num_samples: A scalar

  Returns:
    `model_input`: A tensor of size batch_size x num_samples x feature_size
  """

  batch_size = tf.shape(model_input)[0]
  frame_index_offset = tf.tile(
      tf.expand_dims(tf.range(num_samples), 0), [batch_size, 1])
  max_start_frame_index = tf.maximum(num_frames - num_samples, 0)
  start_frame_index = tf.cast(
      tf.multiply(
          tf.random_uniform([batch_size, 1]),
          tf.cast(max_start_frame_index + 1, tf.float32)), tf.int32)
  frame_index = tf.minimum(start_frame_index + frame_index_offset,
                           tf.cast(num_frames - 1, tf.int32))
  batch_index = tf.tile(
      tf.expand_dims(tf.range(batch_size), 1), [1, num_samples])
  index = tf.stack([batch_index, frame_index], 2)
  return tf.gather_nd(model_input, index)