老师我想知道这个代码输出图像的尺寸大小要怎么做
import tensorflow as tf
import os
import pickle
import numpy as np

CIFAR_DIR = “./…/…/cifar-10-batches-py"
print(os.listdir(CIFAR_DIR))
def load_data(filename):
”"“read data from data file.”""
with open(filename, ‘rb’) as f:
data = pickle.load(f, encoding=‘bytes’)
return data[b’data’], data[b’labels’]

tensorflow.Dataset.

class CifarData:
def init(self, filenames, need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data, labels = load_data(filename)
all_data.append(data)
all_labels.append(labels)
self._data = np.vstack(all_data)
self._data = self._data / 127.5 - 1
self._labels = np.hstack(all_labels)
print(self._data.shape)
print(self._labels.shape)

    self._num_examples = self._data.shape[0]
    self._need_shuffle = need_shuffle
    self._indicator = 0
    if self._need_shuffle:
        self._shuffle_data()
        
def _shuffle_data(self):
    # [0,1,2,3,4,5] -> [5,3,2,4,0,1]
    p = np.random.permutation(self._num_examples)
    self._data = self._data[p]
    self._labels = self._labels[p]

def next_batch(self, batch_size):
    """return batch_size examples as a batch."""
    end_indicator = self._indicator + batch_size
    if end_indicator > self._num_examples:
        if self._need_shuffle:
            self._shuffle_data()
            self._indicator = 0
            end_indicator = batch_size
        else:
            raise Exception("have no more examples")
    if end_indicator > self._num_examples:
        raise Exception("batch size is larger than all examples")
    batch_data = self._data[self._indicator: end_indicator]
    batch_labels = self._labels[self._indicator: end_indicator]
    self._indicator = end_indicator
    return batch_data, batch_labels

train_filenames = [os.path.join(CIFAR_DIR, ‘data_batch_%d’ % i) for i in range(1, 6)]
test_filenames = [os.path.join(CIFAR_DIR, ‘test_batch’)]

train_data = CifarData(train_filenames, True)
test_data = CifarData(test_filenames, False)
def inception_block(x,
output_channel_for_each_path,
name):
""“inception block implementation”""
""“
Args:
- x:
- output_channel_for_each_path: eg: [10, 20, 5]
- name:
”""
with tf.variable_scope(name):
conv1_1 = tf.layers.conv2d(x,
output_channel_for_each_path[0],
(1, 1),
strides = (1,1),
padding = ‘same’,
activation = tf.nn.relu,
name = ‘conv1_1’)
conv3_3 = tf.layers.conv2d(x,
output_channel_for_each_path[1],
(3, 3),
strides = (1,1),
padding = ‘same’,
activation = tf.nn.relu,
name = ‘conv3_3’)
conv5_5 = tf.layers.conv2d(x,
output_channel_for_each_path[2],
(5, 5),
strides = (1,1),
padding = ‘same’,
activation = tf.nn.relu,
name = ‘conv5_5’)
max_pooling = tf.layers.max_pooling2d(x,
(2, 2),
(2, 2),
name = ‘max_pooling’)

max_pooling_shape = max_pooling.get_shape().as_list()[1:]
input_shape = x.get_shape().as_list()[1:]
width_padding = (input_shape[0] - max_pooling_shape[0]) // 2
height_padding = (input_shape[1] - max_pooling_shape[1]) // 2
padded_pooling = tf.pad(max_pooling,
                        [[0, 0],
                         [width_padding, width_padding],
                         [height_padding, height_padding],
                         [0, 0]])
concat_layer = tf.concat(
    [conv1_1, conv3_3, conv5_5, padded_pooling],
    axis = 3)
return concat_layer

x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])

[None], eg: [0,5,6,3]

x_image = tf.reshape(x, [-1, 3, 32, 32])

32*32

x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])

conv1: 神经元图， feature_map, 输出图像

conv1 = tf.layers.conv2d(x_image,
32, # output channel number
(3,3), # kernel size
padding = ‘same’,
activation = tf.nn.relu,
name = ‘conv1’)

pooling1 = tf.layers.max_pooling2d(conv1,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool1’)

inception_2a = inception_block(pooling1,
[16, 16, 16],
name = ‘inception_2a’)
inception_2b = inception_block(inception_2a,
[16, 16, 16],
name = ‘inception_2b’)

pooling2 = tf.layers.max_pooling2d(inception_2b,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool2’)

inception_3a = inception_block(pooling2,
[16, 16, 16],
name = ‘inception_3a’)
inception_3b = inception_block(inception_3a,
[16, 16, 16],
name = ‘inception_3b’)

pooling3 = tf.layers.max_pooling2d(inception_3b,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool3’)

flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)

loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)

y_ -> sofmax

y -> one_hot

loss = ylogy_

indices

predict = tf.argmax(y_, 1)

[1,0,1,1,1,0,0,0]

correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))

with tf.name_scope(‘train_op’):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
init = tf.global_variables_initializer()
batch_size = 20
train_steps = 10000
test_steps = 100

train 10k: 74.65%

with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data, batch_labels = train_data.next_batch(batch_size)
loss_val, acc_val, _ = sess.run(
[loss, accuracy, train_op],
feed_dict={
x: batch_data,
y: batch_labels})
if (i+1) % 100 == 0:
print(’[Train] Step: %d, loss: %4.5f, acc: %4.5f’
% (i+1, loss_val, acc_val))
if (i+1) % 1000 == 0:
test_data = CifarData(test_filenames, False)
all_test_acc_val = []
for j in range(test_steps):
test_batch_data, test_batch_labels
= test_data.next_batch(batch_size)
test_acc_val = sess.run(
[accuracy],
feed_dict = {
x: test_batch_data,
y: test_batch_labels
})
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print (’[Test ] Step: %d, acc: %4.5f’ % (i+1, test_acc))