TensorFlow CNN 相关类与方法

embedding

tf.nn.embedding_lookup(params,ids,...)

通过id找对应的tensor.

在 Text-CNN中, 输入的word对应着一个tensor, 这个参数是可学习的, 就会用到这个函数.

示例:

embedding_shape = [vocab_size, embedding_size]
embedding_weight = tf.Variable(
tf.random_uniform(embedding_shape, -1.0, 1.0),
name="W",
dtype=tf.float32)
embedded_chars = tf.nn.embedding_lookup(self.embedding_weight, input_data)

one-hot

tf.one-hot(indices, depth, on_value=None, off_value=None,axis=None, dtype=None, name=None)

即

tensorflow.python.ops.array_ops.one_hot()

.

Returns a one-hot tensor.

conv2d

tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None,data_format=None, name=None)

即

tensorflow.python.ops.gen_nn_ops.conv2d()

,

Computes a 2-D convolution given 4-D

input

and

filter

tensors. 参数见下:

input

a tensor of shape

[batch, in_height, in_width, in_channels]

filter

a tensor of shape

[filter_height, filter_width, in_channels, out_channels]

strides

A list of

ints

. 1-D tensor of length 4. The stride of the sliding window for each dimension of

input

. 一般情况下, strides=[1,1,1,1].

padding

A

string

from:

"SAME", "VALID"

.

SAME 对应的就是 zero-padding, 卷积后尺寸不变. VALID 对应的就是不填充, 卷积后尺寸为

input_size-filter_size+1

.

Why call it a 2-D convolution, because it flattens the filter to a 2-D matrix with shape

[filter_height * filter_width * in_channels, output_channels]

.

max_pool

tf.nn.max_pool(value, ksize, strides, padding, data_format="NHWC", name=None)

Performs the max pooling on the input. 参数见下:

value

A 4-D

Tensor

with shape

[batch, height, width, channels]

and type

tf.float32

.

ksize

A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.

strides

A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.

一般情况下, strides=[1,ksize[1],ksize[2],1].

fully-connected

使用

tf.matmul()

即可.

argmax

tf.argmax(input,axis=None,name=None,dimension=None,output_type=dtypes.int64)

即

tensorflow.python.ops.math_ops.argmax()

.

类似于 numpy.argmax(), it returns the index with the largest value across dimensions of a tensor.