小白初学tensorflow入手教程——持续更新

看过很多视频和教程，适用与windows和tf2.0版本的教程很少。。决定从tensorflow中文社区重新开始学习。但是也会有很多报错，把诸多修改后的小代码如下所示。 变量：

# -*- coding: utf-8 -*- """ Created on Sat Nov 2 10:59:18 2019 @author: lsf81 """ import tensorflow as tf state=tf.Variable(0,name="counter") one=tf.constant(1) new_value=tf.add(state,one) update=tf.compat.v1.assign(state,new_value) init_op=tf.compat.v1.global_variables_initializer() with tf.compat.v1.Session() as sess: sess.run(init_op) print(sess.run(state)) for _ in range(3): sess.run(update) print(sess.run(state))

运行结果： 注意：原版update = tf.assign(state, new_value)都更新成了update=tf.compat.v1.assign(state,new_value) Fetch:

import tensorflow as tf input1 = tf.constant(3.0) input2 = tf.constant(2.0) input3 = tf.constant(5.0) intermed = tf.compat.v1.add(input2, input3) mul = tf.multiply(input1, intermed) with tf.compat.v1.Session() as sess: result = sess.run([mul, intermed]) print(result)

必须要注意，在现在版本中，mul调用方式变成了multiply,and sub 变成了subtract。 Feed:

```python import tensorflow as tf input1 = tf.placeholder(tf.float32) input2 = tf.placeholder(tf.float32) output = tf.multiply(input1, input2) with tf.compat.v1.Session() as sess: print(sess.run([output],feed_dict={input1:[7.],input2:[2.]}))

注意：官网上是input1 = tf.placeholder(tf.types.float32)，其中types是多余的。

mnist详细版教程 https://blog.csdn.net/cqrtxwd/article/details/79028264

input_data.py下载地址 https://blog.csdn.net/weixin_43159628/article/details/83241345

搞了一下午：最新更新如下： 出现Please use alternatives such as official/mnist/dataset.py from tensorflow/models.的错误。原因是新版本不再支持tensorflow/example/tutorials函数库了，全部更新到keras里面了，具体代码如下

import tensorflow as tf #from tensorflow.examples.tutorials.mnist import input_data #mnist=input_data.read_data_sets('./MNIST_data/',one_hot=True) #sess=tf.InteractiveSession() #print('Training data size: ', mnist.train.num_examples) mnist=tf.keras.datasets.mnist (X_train, y_train), (X_test, y_test) = mnist.load_data() print(X_train.shape) # out: (60000, 28, 28) print(y_train.shape) # out: (60000,)

参考博文：https://blog.csdn.net/u011106767/article/details/93879120 后续问题遇到了keras中mnist分类的问题。见以下博文： https://blog.csdn.net/Yumi_huang/article/details/82351173

参考上面的博文：

在这里插入代# -*- coding: utf-8 -*- """ Created on Sat Nov 2 18:56:47 2019 @author: lsf81 """ import numpy as np import matplotlib.pyplot as plt path = r"F:\python\Anaconda\Lib\site-packages\keras_applications\examples\mnist.npz" f = np.load(path) x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] f.close() print(x_train.shape) print(x_test.shape) for i in range(9): plt.subplot(3,3,i+1) plt.imshow(x_train[i], cmap='gray', interpolation='none') plt.title("Class {}".format(y_train[i])) plt.show() from keras.datasets import mnist from keras.models import Sequential from keras.layers.core import Dense, Activation, Dropout from keras.utils import np_utils import numpy as np import matplotlib.pyplot as plt from keras.optimizers import RMSprop path = r"F:\python\Anaconda\Lib\site-packages\keras_applications\examples\mnist.npz" f = np.load(path) x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] f.close() # print(x_train.shape) # print(x_test.shape) # for i in range(9): # plt.subplot(3,3,i+1) # plt.imshow(x_train[i], cmap='gray', interpolation='none') # plt.title("Class {}".format(y_train[i])) # plt.show() #将二维数据变为一维 X_train = x_train.reshape(len(x_train), -1) X_test = x_test.reshape(len(x_test), -1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') # Normalization.scaling it so that all values are in the [0, 1] interval. X_train = (X_train - 127) / 127 X_test = (X_test - 127) / 127 #one hot encoding y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) model = Sequential([ Dense(512, input_dim=784), Activation('relu'), Dropout(0.2), # Dense(512), # Activation('relu'), # Dropout(0.2), Dense(10), Activation('softmax'), ]) rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) # We add metrics to get more results you want to see model.compile(optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy']) print('Training ------------') # Another way to train the model model.fit(X_train, y_train, epochs=20, batch_size=100) print('\nTesting ------------') # Evaluate the model with the metrics we defined earlier loss, accuracy = model.evaluate(X_test, y_test) print('test loss: ', loss) print('test accuracy: ', accuracy) 码片

最后的结果为：

runfile('E:/Users/lsf81/Desktop/cnnmnist2.py', wdir='E:/Users/lsf81/Desktop') (60000, 28, 28) (10000, 28, 28)  Using TensorFlow backend. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead. WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead. Training ------------ WARNING:tensorflow:From F:\python\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where Epoch 1/20 60000/60000 [==============================] - 5s 79us/step - loss: 0.4220 - acc: 0.8750 Epoch 2/20 60000/60000 [==============================] - 3s 58us/step - loss: 0.1797 - acc: 0.9443 Epoch 3/20 60000/60000 [==============================] - 5s 80us/step - loss: 0.1379 - acc: 0.9586 Epoch 4/20 60000/60000 [==============================] - 9s 146us/step - loss: 0.1224 - acc: 0.9636 Epoch 5/20 60000/60000 [==============================] - 9s 155us/step - loss: 0.1066 - acc: 0.9683 Epoch 6/20 60000/60000 [==============================] - 9s 148us/step - loss: 0.1005 - acc: 0.9701 Epoch 7/20 60000/60000 [==============================] - 9s 154us/step - loss: 0.0929 - acc: 0.9728 Epoch 8/20 60000/60000 [==============================] - 9s 155us/step - loss: 0.0866 - acc: 0.9754 Epoch 9/20 60000/60000 [==============================] - 9s 145us/step - loss: 0.0829 - acc: 0.9761 Epoch 10/20 60000/60000 [==============================] - 9s 154us/step - loss: 0.0766 - acc: 0.9773 Epoch 11/20 60000/60000 [==============================] - 9s 147us/step - loss: 0.0740 - acc: 0.9789 Epoch 12/20 60000/60000 [==============================] - 9s 153us/step - loss: 0.0683 - acc: 0.9814 Epoch 13/20 60000/60000 [==============================] - 7s 118us/step - loss: 0.0698 - acc: 0.9808 Epoch 14/20 60000/60000 [==============================] - 10s 158us/step - loss: 0.0652 - acc: 0.9823 Epoch 15/20 60000/60000 [==============================] - 9s 153us/step - loss: 0.0637 - acc: 0.9830 Epoch 16/20 60000/60000 [==============================] - 9s 144us/step - loss: 0.0634 - acc: 0.9831 Epoch 17/20 60000/60000 [==============================] - 9s 153us/step - loss: 0.0605 - acc: 0.9836 Epoch 18/20 60000/60000 [==============================] - 8s 140us/step - loss: 0.0559 - acc: 0.9845 Epoch 19/20 60000/60000 [==============================] - 9s 149us/step - loss: 0.0569 - acc: 0.9848 Epoch 20/20 60000/60000 [==============================] - 7s 118us/step - loss: 0.0552 - acc: 0.9859 Testing ------------ 10000/10000 [==============================] - 1s 63us/step test loss: 0.11286064563859545 test accuracy: 0.9791

刚刚试着修改一下那些警告，升完级后发现不能运行了。又把版本改回来了，暂时先这样。自己的第一个程序。 上面mnist博客中的程序，已经跑完，代码如下

import input_data import tensorflow as tf #读取数据 mnist = input_data.read_data_sets('MNIST_data', one_hot=True) sess=tf.compat.v1.InteractiveSession() #构建cnn网络结构 #自定义卷积函数（后面卷积时就不用写太多） def conv2d(x,w): return tf.nn.conv2d(x,w,strides=[1,1,1,1],padding='SAME') #自定义池化函数 def max_pool_2x2(x): return tf.nn.max_pool2d(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') #设置占位符，尺寸为样本输入和输出的尺寸 x=tf.compat.v1.placeholder(tf.float32,[None,784]) y_=tf.placeholder(tf.float32,[None,10]) x_img=tf.reshape(x,[-1,28,28,1]) #设置第一个卷积层和池化层 w_conv1=tf.Variable(tf.random.truncated_normal([3,3,1,32],stddev=0.1)) b_conv1=tf.Variable(tf.constant(0.1,shape=[32])) h_conv1=tf.nn.relu(conv2d(x_img,w_conv1)+b_conv1) h_pool1=max_pool_2x2(h_conv1) #设置第二个卷积层和池化层 w_conv2=tf.Variable(tf.truncated_normal([3,3,32,50],stddev=0.1)) b_conv2=tf.Variable(tf.constant(0.1,shape=[50])) h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2) h_pool2=max_pool_2x2(h_conv2) #设置第一个全连接层 w_fc1=tf.Variable(tf.truncated_normal([7*7*50,1024],stddev=0.1)) b_fc1=tf.Variable(tf.constant(0.1,shape=[1024])) h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*50]) h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1) #dropout（随机权重失活） keep_prob=tf.placeholder(tf.float32) h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) #设置第二个全连接层 w_fc2=tf.Variable(tf.truncated_normal([1024,10],stddev=0.1)) b_fc2=tf.Variable(tf.constant(0.1,shape=[10])) y_out=tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2)+b_fc2) #建立loss function，为交叉熵 loss=tf.reduce_mean(-tf.reduce_sum(y_*tf.math.log(y_out),reduction_indices=[1])) #配置Adam优化器，学习速率为1e-4 train_step=tf.compat.v1.train.AdamOptimizer(1e-4).minimize(loss) #建立正确率计算表达式 correct_prediction=tf.equal(tf.argmax(y_out,1),tf.argmax(y_,1)) accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #开始喂数据，训练 tf.compat.v1.global_variables_initializer().run() for i in range(20000): batch=mnist.train.next_batch(50) if i%100==0: train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1}) print("step %d,train_accuracy= %g"%(i,train_accuracy)) train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5}) #训练之后，使用测试集进行测试，输出最终结果 print("test_accuracy= %g"%accuracy.eval(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1}))

结果为

F:\python\pycharm\venv\Scripts\python.exe F:/python/code/cnntest1.py Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting MNIST_data\train-images-idx3-ubyte.gz Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Extracting MNIST_data\train-labels-idx1-ubyte.gz Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting MNIST_data\t10k-images-idx3-ubyte.gz Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting MNIST_data\t10k-labels-idx1-ubyte.gz WARNING:tensorflow:From F:/python/code/cnntest1.py:5: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead. 2019-11-05 09:27:27.623410: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 WARNING:tensorflow:From F:/python/code/cnntest1.py:14: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. WARNING:tensorflow:From F:/python/code/cnntest1.py:19: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead. WARNING:tensorflow:From F:/python/code/cnntest1.py:12: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead. WARNING:tensorflow:From F:/python/code/cnntest1.py:38: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. WARNING:tensorflow:From F:/python/code/cnntest1.py:46: The name tf.log is deprecated. Please use tf.math.log instead. WARNING:tensorflow:From F:/python/code/cnntest1.py:48: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. WARNING:tensorflow:From F:/python/code/cnntest1.py:55: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead. step 0,train_accuracy= 0.08 step 100,train_accuracy= 0.84 step 200,train_accuracy= 0.92 step 300,train_accuracy= 0.84 step 400,train_accuracy= 0.94 step 500,train_accuracy= 0.84 step 600,train_accuracy= 1 step 700,train_accuracy= 0.94 step 800,train_accuracy= 0.9 step 900,train_accuracy= 1 step 1000,train_accuracy= 0.96 step 1100,train_accuracy= 0.96 step 1200,train_accuracy= 0.96 step 1300,train_accuracy= 0.94 step 1400,train_accuracy= 0.94 step 1500,train_accuracy= 0.96 step 1600,train_accuracy= 0.96 step 1700,train_accuracy= 0.96 step 1800,train_accuracy= 0.98 step 1900,train_accuracy= 0.96 step 2000,train_accuracy= 0.98 step 2100,train_accuracy= 1 step 2200,train_accuracy= 0.96 step 2300,train_accuracy= 1 step 2400,train_accuracy= 1 step 2500,train_accuracy= 0.96 step 2600,train_accuracy= 0.98 step 2700,train_accuracy= 0.98 step 2800,train_accuracy= 0.94 step 2900,train_accuracy= 0.96 step 3000,train_accuracy= 0.98 step 3100,train_accuracy= 0.98 step 3200,train_accuracy= 0.94 step 3300,train_accuracy= 1 step 3400,train_accuracy= 1 step 3500,train_accuracy= 1 step 3600,train_accuracy= 0.96 step 3700,train_accuracy= 1 step 3800,train_accuracy= 0.98 step 3900,train_accuracy= 0.96 step 4000,train_accuracy= 0.98 step 4100,train_accuracy= 0.96 step 4200,train_accuracy= 0.96 step 4300,train_accuracy= 0.98 step 4400,train_accuracy= 1 step 4500,train_accuracy= 0.98 step 4600,train_accuracy= 0.96 step 4700,train_accuracy= 0.96 step 4800,train_accuracy= 0.92 step 4900,train_accuracy= 0.96 step 5000,train_accuracy= 1 step 5100,train_accuracy= 1 step 5200,train_accuracy= 1 step 5300,train_accuracy= 1 step 5400,train_accuracy= 1 step 5500,train_accuracy= 0.98 step 5600,train_accuracy= 1 step 5700,train_accuracy= 0.98 step 5800,train_accuracy= 0.98 step 5900,train_accuracy= 1 step 6000,train_accuracy= 1 step 6100,train_accuracy= 1 step 6200,train_accuracy= 1 step 6300,train_accuracy= 1 step 6400,train_accuracy= 0.96 step 6500,train_accuracy= 0.98 step 6600,train_accuracy= 0.94 step 6700,train_accuracy= 1 step 6800,train_accuracy= 1 step 6900,train_accuracy= 1 step 7000,train_accuracy= 1 step 7100,train_accuracy= 0.98 step 7200,train_accuracy= 1 step 7300,train_accuracy= 1 step 7400,train_accuracy= 1 step 7500,train_accuracy= 1 step 7600,train_accuracy= 1 step 7700,train_accuracy= 1 step 7800,train_accuracy= 1 step 7900,train_accuracy= 1 step 8000,train_accuracy= 0.98 step 8100,train_accuracy= 1 step 8200,train_accuracy= 0.96 step 8300,train_accuracy= 0.98 step 8400,train_accuracy= 1 step 8500,train_accuracy= 1 step 8600,train_accuracy= 0.98 step 8700,train_accuracy= 1 step 8800,train_accuracy= 1 step 8900,train_accuracy= 1 step 9000,train_accuracy= 1 step 9100,train_accuracy= 1 step 9200,train_accuracy= 1 step 9300,train_accuracy= 1 step 9400,train_accuracy= 1 step 9500,train_accuracy= 1 step 9600,train_accuracy= 1 step 9700,train_accuracy= 0.98 step 9800,train_accuracy= 1 step 9900,train_accuracy= 0.98 step 10000,train_accuracy= 0.98 step 10100,train_accuracy= 1 step 10200,train_accuracy= 0.96 step 10300,train_accuracy= 1 step 10400,train_accuracy= 1 step 10500,train_accuracy= 1 step 10600,train_accuracy= 0.98 step 10700,train_accuracy= 1 step 10800,train_accuracy= 1 step 10900,train_accuracy= 0.98 step 11000,train_accuracy= 0.98 step 11100,train_accuracy= 1 step 11200,train_accuracy= 1 step 11300,train_accuracy= 0.98 step 11400,train_accuracy= 1 step 11500,train_accuracy= 0.98 step 11600,train_accuracy= 1 step 11700,train_accuracy= 0.98 step 11800,train_accuracy= 1 step 11900,train_accuracy= 0.98 step 12000,train_accuracy= 1 step 12100,train_accuracy= 1 step 12200,train_accuracy= 0.96 step 12300,train_accuracy= 0.98 step 12400,train_accuracy= 1 step 12500,train_accuracy= 1 step 12600,train_accuracy= 1 step 12700,train_accuracy= 1 step 12800,train_accuracy= 1 step 12900,train_accuracy= 1 step 13000,train_accuracy= 1 step 13100,train_accuracy= 1 step 13200,train_accuracy= 1 step 13300,train_accuracy= 1 step 13400,train_accuracy= 1 step 13500,train_accuracy= 1 step 13600,train_accuracy= 1 step 13700,train_accuracy= 1 step 13800,train_accuracy= 1 step 13900,train_accuracy= 1 step 14000,train_accuracy= 0.98 step 14100,train_accuracy= 1 step 14200,train_accuracy= 1 step 14300,train_accuracy= 0.98 step 14400,train_accuracy= 1 step 14500,train_accuracy= 1 step 14600,train_accuracy= 1 step 14700,train_accuracy= 1 step 14800,train_accuracy= 0.98 step 14900,train_accuracy= 1 step 15000,train_accuracy= 1 step 15100,train_accuracy= 1 step 15200,train_accuracy= 1 step 15300,train_accuracy= 1 step 15400,train_accuracy= 1 step 15500,train_accuracy= 1 step 15600,train_accuracy= 1 step 15700,train_accuracy= 1 step 15800,train_accuracy= 1 step 15900,train_accuracy= 1 step 16000,train_accuracy= 1 step 16100,train_accuracy= 1 step 16200,train_accuracy= 1 step 16300,train_accuracy= 1 step 16400,train_accuracy= 1 step 16500,train_accuracy= 1 step 16600,train_accuracy= 1 step 16700,train_accuracy= 1 step 16800,train_accuracy= 1 step 16900,train_accuracy= 1 step 17000,train_accuracy= 1 step 17100,train_accuracy= 1 step 17200,train_accuracy= 1 step 17300,train_accuracy= 1 step 17400,train_accuracy= 1 step 17500,train_accuracy= 1 step 17600,train_accuracy= 1 step 17700,train_accuracy= 1 step 17800,train_accuracy= 0.98 step 17900,train_accuracy= 1 step 18000,train_accuracy= 1 step 18100,train_accuracy= 1 step 18200,train_accuracy= 1 step 18300,train_accuracy= 1 step 18400,train_accuracy= 1 step 18500,train_accuracy= 1 step 18600,train_accuracy= 1 step 18700,train_accuracy= 1 step 18800,train_accuracy= 1 step 18900,train_accuracy= 1 step 19000,train_accuracy= 1 step 19100,train_accuracy= 1 step 19200,train_accuracy= 1 step 19300,train_accuracy= 1 step 19400,train_accuracy= 1 step 19500,train_accuracy= 1 step 19600,train_accuracy= 1 step 19700,train_accuracy= 1 step 19800,train_accuracy= 1 step 19900,train_accuracy= 1 2019-11-05 09:41:01.897069: W tensorflow/core/framework/allocator.cc:107] Allocation of 1003520000 exceeds 10% of system memory. 2019-11-05 09:41:02.535947: W tensorflow/core/framework/allocator.cc:107] Allocation of 250880000 exceeds 10% of system memory. 2019-11-05 09:41:02.763879: W tensorflow/core/framework/allocator.cc:107] Allocation of 392000000 exceeds 10% of system memory. test_accuracy= 0.9908 Process finished with exit code 0