TensorFlow实现卷积操作和池化操作

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Tensorflow实现卷积操作

设定 strides 、 padding 、 filter weight/bias (F_w and F_b) 输出是 (1, 2, 2, 3)。除了 strides 所有变量的类型都应该为 TensorFlow 变量。代码如下：

"""
设定strides,padding和卷积核参数weight/bias，并且最后tensor输出形状为(1,2,2,3)
"""
import tensorflow as tf
import numpy as np

# `tf.nn.conv2d` requires the input be 4D (batch_size, height, width, depth)
# (1, 4, 4, 1)
x = np.array([
    [0, 1, 0.5, 10],
    [2, 2.5, 1, -8],
    [4, 0, 5, 6],
    [15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
X = tf.constant(x)


def conv2d(input):
    # Filter (weights and bias)
    # The shape of the filter weight is (height, width, input_depth, output_depth)
    # The shape of the filter bias is (output_depth,)
    # TODO: Define the filter weights `F_W` and filter bias `F_b`.
    # NOTE: Remember to wrap them in `tf.Variable`, they are trainable parameters after all.
    F_W = tf.Variable(tf.truncated_normal((2, 2, 1, 3)))
    F_b = tf.Variable(tf.zeros(3))
    # TODO: Set the stride for each dimension (batch_size, height, width, depth)
    strides = [1, 2, 2, 1]
    # TODO: set the padding, either 'VALID' or 'SAME'.
    padding = 'SAME'
    # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#conv2d
    # `tf.nn.conv2d` does not include the bias computation so we have to add it ourselves after.
    return tf.nn.conv2d(input, F_W, strides, padding) + F_b

out = conv2d(X)

Tensorflow实现池化操作

设定 strides，padding 和 ksize 使得池化的结果维度为 (1, 2, 2, 1)。代码如下：

"""
Set the values to `strides` and `ksize` such that
the output shape after pooling is (1, 2, 2, 1).
"""
import tensorflow as tf
import numpy as np

# `tf.nn.max_pool` requires the input be 4D (batch_size, height, width, depth)
# (1, 4, 4, 1)
x = np.array([
    [0, 1, 0.5, 10],
    [2, 2.5, 1, -8],
    [4, 0, 5, 6],
    [15, 1, 2, 3]], dtype=np.float32).reshape((1, 4, 4, 1))
X = tf.constant(x)

def maxpool(input):
    # TODO: Set the ksize (filter size) for each dimension (batch_size, height, width, depth)
    ksize = [1, 2, 2, 1]
    # TODO: Set the stride for each dimension (batch_size, height, width, depth)
    strides = [1, 2, 2, 1]
    # TODO: set the padding, either 'VALID' or 'SAME'.
    padding = 'SAME'
    # https://www.tensorflow.org/versions/r0.11/api_docs/python/nn.html#max_pool
    return tf.nn.max_pool(input, ksize, strides, padding)

out = maxpool(X)