报了这个错是为啥
D:\ProgramData\Anaconda3\envs\python3.6\python.exe C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\pydev_run_in_console.py 61990 61991 D:/IdeaProjects/TensorFlow_Exercises/HelloWorld/cnn_mnist.py
Running D:/IdeaProjects/TensorFlow_Exercises/HelloWorld/cnn_mnist.py
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['D:\\IdeaProjects\\TensorFlow_Exercises', 'D:/IdeaProjects/TensorFlow_Exercises/HelloWorld'])
WARNING:tensorflow:From D:/IdeaProjects/TensorFlow_Exercises/HelloWorld/cnn_mnist.py:6: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting mnist_data\train-images-idx3-ubyte.gz
Extracting mnist_data\train-labels-idx1-ubyte.gz
WARNING:tensorflow:From D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting mnist_data\t10k-images-idx3-ubyte.gz
WARNING:tensorflow:From D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting mnist_data\t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\contrib\learn\python\learn\datasets\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
2018-06-20 17:49:02.072827: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
Traceback (most recent call last):
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1322, in _do_call
return fn(*args)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1307, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must be broadcastable: logits_size=[100,10] labels_size=[50,10]
[[Node: softmax_cross_entropy_loss/xentropy = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](softmax_cross_entropy_loss/xentropy/Reshape, softmax_cross_entropy_loss/xentropy/Reshape_1)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\pydev_run_in_console.py", line 52, in run_file
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/IdeaProjects/TensorFlow_Exercises/HelloWorld/cnn_mnist.py", line 159, in <module>
{input_x: batch[0], output_y: batch[1]}
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 900, in run
run_metadata_ptr)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run
run_metadata)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must be broadcastable: logits_size=[100,10] labels_size=[50,10]
[[Node: softmax_cross_entropy_loss/xentropy = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](softmax_cross_entropy_loss/xentropy/Reshape, softmax_cross_entropy_loss/xentropy/Reshape_1)]]
Caused by op 'softmax_cross_entropy_loss/xentropy', defined at:
File "C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\pydev_run_in_console.py", line 150, in <module>
globals = run_file(file, None, None, is_module)
File "C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\pydev_run_in_console.py", line 52, in run_file
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Users\Administrator\.IntelliJIdea2018.1\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "D:/IdeaProjects/TensorFlow_Exercises/HelloWorld/cnn_mnist.py", line 124, in <module>
onehot_labels=output_y, logits=logits
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\ops\losses\losses_impl.py", line 749, in softmax_cross_entropy
labels=onehot_labels, logits=logits, name="xentropy")
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1873, in softmax_cross_entropy_with_logits_v2
precise_logits, labels, name=name)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 7168, in softmax_cross_entropy_with_logits
name=name)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "D:\ProgramData\Anaconda3\envs\python3.6\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): logits and labels must be broadcastable: logits_size=[100,10] labels_size=[50,10]
[[Node: softmax_cross_entropy_loss/xentropy = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](softmax_cross_entropy_loss/xentropy/Reshape, softmax_cross_entropy_loss/xentropy/Reshape_1)]]
PyDev console: starting.
Python 3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 12:30:02) [MSC v.1900 64 bit (AMD64)] on win326580
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3回答
Oscar
2018-06-20 18:35:55
我的代码被你自己改过了，然后有好几个地方错误：
你的代码开始处没有 # -*- coding: UTF-8 -*-
pool2 的 inputs 应该是 conv2，你写了 conv1
conv2 的 inputs 应该是 pool1，你写了 input_x_images
test_output = sess.run(logits, {input_x: test_x[:20]}) 这句里 input_x 后面是冒号（:），你写了 逗号（,）
最后那个测试的代码块（“# 测试：打印 20 个预测值 和 真实值”到最后），应该没有缩进才对。
正确的代码应该是：
# -*- coding: UTF-8 -*-
import tensorflow as tf
import numpy as np
# 下载并载入MNIST 手写数字库（55000 * 28 * 28） 55000张训练图像
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('mnist_data', one_hot=True)
# one_hot 独热码的编码(encoding) 心事
# 0,1,2,3,4,5,6,7,8,9 的十位数字
# 独热码会把这10位数字以一种独特的形式来表示
# 0 : 1000000000
# 1 : 0100000000
# 2 : 0010000000
# 3 : 0001000000
# 4 : 0000100000
# 5 : 0000010000
# 省略...
# 构建测试的数据
# None 表示张量(Tensor)的第一个维度可以是任何长度
# 除以255 是因为黑白图片(灰度图像)有0-255的一个灰度值的范围
input_x = tf.placeholder(tf.float32, [None, 28 * 28]) / 255.
# 输出:10个数字的标签
output_y = tf.placeholder(tf.int32, [None, 10])
# 改变形状之后的输入
input_x_images = tf.reshape(input_x, [-1, 28, 28, 1])
# 训练的数据库，测试的数据库，验证的数据库
# 这里用到训练的和测试的数据库
# 从 Test(测试)数据集里选取3000个手写数字的图片和对应标签
# 图片
test_x = mnist.test.images[:3000]
# 标签
test_y = mnist.test.labels[:3000]
# 构建我们的卷积神经网络
# conv:卷积 2d:二维
# 第 1 层卷积
conv1 = tf.layers.conv2d(
# 形状是28*28*1
inputs=input_x_images,
# 32个过滤器,输出的深度(depth) 是32
filters=32,
# 过滤器在二维的大小是(5 * 5)
kernel_size=[5, 5],
# 步长是1(每跨一步采样一次)
strides=1,
# same表示输出的大小不变，因此需要在外围补零2圈
padding='same',
# 激活函数是Relu
activation=tf.nn.relu
# 形状变成28*28*32
)
# 池化层(亚采样)：只采集一部分数据
# Pool:2x2 stride:2
# 第 1 层池化(亚采样)
pool1 = tf.layers.max_pooling2d(
# 形状 28*28*32
inputs=conv1,
# 过滤器在二维的大小是(2 * 2)
pool_size=[2, 2],
# 步长是2
strides=2
# 形状 [14, 14, 32]
)
# 第 2 层卷积
conv2 = tf.layers.conv2d(
# 形状是14*14*32
inputs=pool1,
# 64个过滤器,输出的深度(depth) 是64
filters=64,
# 过滤器在二维的大小是(5 * 5)
kernel_size=[5, 5],
# 步长是1(每跨一步采样一次)
strides=1,
# same表示输出的大小不变，因此需要在外围补零2圈
padding='same',
# 激活函数是Relu
activation=tf.nn.relu
# 形状变成14*14*64
)
# Pool:2x2 stride:2
# 第 2 层池化(亚采样)
pool2 = tf.layers.max_pooling2d(
# 形状 14*14*64
inputs=conv2,
# 过滤器在二维的大小是(2 * 2)
pool_size=[2, 2],
# 步长是2
strides=2
# 形状 [7, 7, 64]
)
# 平坦化(flat) 压成1*1
# 形状 [7 * 7 * 64]
flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
# 1024 个神经元的全连接层
# dense : 密集
dense = tf.layers.dense(
inputs=flat,
# 1024个神经元
units=1024,
# 激活函数
activation=tf.nn.relu
)
# Dropout : 丢弃 50%, 丢弃率: 0.5
dropout = tf.layers.dropout(
inputs=dense,
# 丢弃率
rate=0.5
)
# 10 个神经元的全连接层, 这里不用激活函数来做非线性化了
# 输出。形状[1, 1, 10]
logits = tf.layers.dense(inputs=dropout, units=10)
# 计算误差 (计算 Cross entropy(交叉熵), 再用 softmax 激活函数计算出
# 百分比概率)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=output_y, logits=logits
)
# Adam优化器来最小化误差, 学习率 0.001
# minimize 最小化预测值和真实值的误差，越小越好
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# 精度。计算 预测值 和 实际标签的匹配程度
# (accuracy, update_op), 会创建2个局部变量
# 返回,会返回2个值:第一个是精度，第二个是不断更新的操作(不断改变的精度)
accuracy = tf.metrics.accuracy(
# argmax 返回所有维度的轴上面的最大的那个下标
labels=tf.argmax(output_y, axis=1),
# 预估
predictions=tf.argmax(logits, axis=1)
)[1]
# 创建一个会话
with tf.Session() as sess:
# 初始化变量、创建组
init = tf.group(
# 全局初始化
tf.global_variables_initializer(),
# 局部初始化
tf.local_variables_initializer()
)
sess.run(init)
# 训练2w次
for i in range(20000):
# 从 Train (训练) 数据集里取下一个50个样本
batch = mnist.train.next_batch(50)
# 计算误差 和 Adam 优化器
train_loss, train_op_ = sess.run(
[loss, train_op],
{input_x: batch[0], output_y: batch[1]}
)
if i % 100 == 0:
test_accuracy = sess.run(accuracy,
{input_x: test_x,
output_y: test_y
})
print("Step=%d, Train loss=%.4f, [Test accuracy=%.2f]"
% (i, train_loss, test_accuracy))
# 测试 : 打印 20 个预测值 和 真实值的对
test_output = sess.run(logits, {input_x: test_x[:20]})
inferenced_y = np.argmax(test_output, 1)
# 推测的数字
print(inferenced_y, 'Inferenced numbers')
# 真实的数字
print(np.argmax(test_y[:20], 1), 'Real numbers')
0
有任何疑惑可以回复我~
用代码把梦想照进现实
2018-06-20 17:47:30
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