Python tensorflow.python.framework.ops 模块，convert_to_tensor() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用tensorflow.python.framework.ops.convert_to_tensor()。

项目：A3C 作者：go2sea | 项目源码 | 文件源码

def dense(inputs, units, bias_shape, w_i, b_i=None, activation=tf.nn.relu):
    # ??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)
    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)
        return activation(dense + biases) if activation is not None else dense + biases
    return activation(dense) if activation is not None else dense

项目：TAC-GAN 作者：dashayushman | 项目源码 | 文件源码

def binary_cross_entropy(preds, targets, name=None):
    """Computes binary cross entropy given `preds`.

    For brevity, let `x = `, `z = targets`.  The logistic loss is

        loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

    Args:
        preds: A `Tensor` of type `float32` or `float64`.
        targets: A `Tensor` of the same type and shape as `preds`.
    """
    eps = 1e-12
    with ops.op_scope([preds, targets], name, "bce_loss") as name:
        preds = ops.convert_to_tensor(preds, name="preds")
        targets = ops.convert_to_tensor(targets, name="targets")
        return tf.reduce_mean(-(targets * tf.log(preds + eps) +
                              (1. - targets) * tf.log(1. - preds + eps)))

项目：Magic-Pixel 作者：zhwhong | 项目源码 | 文件源码

def binary_cross_entropy(preds, targets, name=None):
    """Computes binary cross entropy given `preds`.

    For brevity, let `x = `, `z = targets`.  The logistic loss is

        loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

    Args:
        preds: A `Tensor` of type `float32` or `float64`.
        targets: A `Tensor` of the same type and shape as `preds`.
    """
    eps = 1e-12
    with ops.op_scope([preds, targets], name, "bce_loss") as name:
        preds = ops.convert_to_tensor(preds, name="preds")
        targets = ops.convert_to_tensor(targets, name="targets")
        return tf.reduce_mean(-(targets * tf.log(preds + eps) +
                              (1. - targets) * tf.log(1. - preds + eps)))

项目：DeepVideo 作者：AniketBajpai | 项目源码 | 文件源码

def binary_cross_entropy(preds, targets, name=None):
    '''Computes binary cross entropy given `preds`.
    Let `x = `, `z = targets`.  The logistic loss is
        loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

    Args:
        preds: A `Tensor` of type `float32` or `float64`.
        targets: A `Tensor` of the same type and shape as `preds`.
    '''
    eps = 1e-12
    with ops.op_scope([preds, targets], name, 'bce_loss') as name:
        preds = ops.convert_to_tensor(preds, name='preds')
        targets = ops.convert_to_tensor(targets, name='targets')
        return tf.reduce_mean(-(targets * tf.log(preds + eps) + (1. - targets) * tf.log(1. - preds + eps)))

# ==================================
# ---------- LAYER MAPS --------- #
# ==================================

项目：Question-Answering 作者：MurtyShikhar | 项目源码 | 文件源码

def hardmax(logits, name=None):
  """Returns batched one-hot vectors.

  The depth index containing the `1` is that of the maximum logit value.

  Args:
    logits: A batch tensor of logit values.
    name: Name to use when creating ops.
  Returns:
    A batched one-hot tensor.
  """
  with ops.name_scope(name, "Hardmax", [logits]):
    logits = ops.convert_to_tensor(logits, name="logits")
    if logits.get_shape()[-1].value is not None:
      depth = logits.get_shape()[-1].value
    else:
      depth = array_ops.shape(logits)[-1]
    return array_ops.one_hot(
        math_ops.argmax(logits, -1), depth, dtype=logits.dtype)

项目：opt-mmd 作者：dougalsutherland | 项目源码 | 文件源码

def binary_cross_entropy(preds, targets, name=None):
    """Computes binary cross entropy given `preds`.

    For brevity, let `x = `, `z = targets`.  The logistic loss is

        loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

    Args:
        preds: A `Tensor` of type `float32` or `float64`.
        targets: A `Tensor` of the same type and shape as `preds`.
    """
    eps = 1e-12
    with ops.op_scope([preds, targets], name, "bce_loss") as name:
        preds = ops.convert_to_tensor(preds, name="preds")
        targets = ops.convert_to_tensor(targets, name="targets")
        return tf.reduce_mean(-(targets * tf.log(preds + eps) +
                              (1. - targets) * tf.log(1. - preds + eps)))

项目：ReverseGAN 作者：SubarnaTripathi | 项目源码 | 文件源码

def binary_cross_entropy(preds, targets, name=None):
    """Computes binary cross entropy given `preds`.

    For brevity, let `x = `, `z = targets`.  The logistic loss is

        loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))

    Args:
        preds: A `Tensor` of type `float32` or `float64`.
        targets: A `Tensor` of the same type and shape as `preds`.
    """
    eps = 1e-12
    with ops.op_scope([preds, targets], name, "bce_loss") as name:
        preds = ops.convert_to_tensor(preds, name="preds")
        targets = ops.convert_to_tensor(targets, name="targets")
        return tf.reduce_mean(-(targets * tf.log(preds + eps) +
                              (1. - targets) * tf.log(1. - preds + eps)))