本文主要是介绍关于tensorflow2.0-alpha0版本，尝鲜，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
关于主要更新的一些情况，可以参看这篇博客
我在这里只是想要跑一个mnist的小demo
主要代码如下（官网代码）
from __future__ import absolute_import, division, print_function
# -*- coding: utf-8 -*-
# !pip install tensorflow-gpu==2.0.0-alpha0 ## 这行代码是安装的，如果已经安装，可以注释
import tensorflow_datasets as tfds # 这个包需要单独安装 pip install tensorflow_datasets
import tensorflow as tffrom tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model"""Load and prepare the [MNIST dataset](http://yann.lecun.com/exdb/mnist/). Convert the samples from integers to floating-point numbers:"""
dataset, info = tfds.load('mnist', with_info=True, as_supervised=True)
mnist_train, mnist_test = dataset['train'], dataset['test']def convert_types(image, label):image = tf.cast(image, tf.float32)image /= 255return image, labelmnist_train = mnist_train.map(convert_types).shuffle(10000).batch(32)
mnist_test = mnist_test.map(convert_types).batch(32)"""Build the `tf.keras` model using the Keras
[model subclassing API](https://www.tensorflow.org/guide/keras#model_subclassing):"""
class MyModel(Model):def __init__(self):super(MyModel, self).__init__()self.conv1 = Conv2D(32, 3, activation='relu')self.flatten = Flatten()self.d1 = Dense(128, activation='relu')self.d2 = Dense(10, activation='softmax')def call(self, x):x = self.conv1(x)x = self.flatten(x)x = self.d1(x)return self.d2(x)model = MyModel()"""Choose an optimizer and loss function for training:"""
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()"""Select metrics to measure the loss and the accuracy of the model. These metrics accumulate the values over epochs and then print the overall result."""
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')"""Train the model using `tf.GradientTape`:"""
@tf.function # 这个装饰器的作用，目前不是很清楚
def train_step(image, label):with tf.GradientTape() as tape:predictions = model(image)loss = loss_object(label, predictions)gradients = tape.gradient(loss, model.trainable_variables)optimizer.apply_gradients(zip(gradients, model.trainable_variables))train_loss(loss)train_accuracy(label, predictions)"""Now test the model:"""
@tf.function
def test_step(image, label):predictions = model(image)t_loss = loss_object(label, predictions)test_loss(t_loss)test_accuracy(label, predictions)EPOCHS = 5for epoch in range(EPOCHS):for image, label in mnist_train:train_step(image, label)for test_image, test_label in mnist_test:test_step(test_image, test_label)template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'print (template.format(epoch+1,train_loss.result(),train_accuracy.result()*100,test_loss.result(),test_accuracy.result()*100))"""The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the [TensorFlow tutorials](https://www.tensorflow.org/alpha/tutorials/keras)."""
输出结果：
D:\Anaconda3\envs\tensorflow\python.exe E:/pythonProgram/Tensorflow/tensorflow2/advanced.py
Downloading / extracting dataset mnist (11.06 MiB) to C:\Users\Administrator\tensorflow_datasets\mnist\1.0.0...
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Dl Size...: 100%|██████████| 10/10 [06:49<00:00, 42.62s/ MiB]Extraction completed...: 100%|██████████| 4/4 [06:49<00:00, 115.90s/ file]60000 examples [00:20, 2938.42 examples/s]
Shuffling...: 0%| | 0/10 [00:00<?, ? shard/s]WARNING: Logging before flag parsing goes to stderr.
W0330 15:32:55.947226 7452 deprecation.py:323] From D:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow_datasets\core\file_format_adapter.py:249: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
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2019-03-30 15:33:07.617894: W .\tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.
Epoch 1, Loss: 0.1331276297569275, Accuracy: 95.9949951171875, Test Loss: 0.0622599795460701, Test Accuracy: 97.94999694824219
2019-03-30 15:34:36.883000: W .\tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.
Epoch 2, Loss: 0.087999127805233, Accuracy: 97.3316650390625, Test Loss: 0.05821216478943825, Test Accuracy: 98.04999542236328
2019-03-30 15:35:55.022469: W .\tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.
Epoch 3, Loss: 0.06530821323394775, Accuracy: 98.00444030761719, Test Loss: 0.05837463214993477, Test Accuracy: 98.15333557128906
2019-03-30 15:37:11.460841: W .\tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.
Epoch 4, Loss: 0.052207402884960175, Accuracy: 98.39083862304688, Test Loss: 0.06129618361592293, Test Accuracy: 98.15250396728516
2019-03-30 15:38:27.955216: W .\tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.
Epoch 5, Loss: 0.04351149871945381, Accuracy: 98.65533447265625, Test Loss: 0.06512587517499924, Test Accuracy: 98.13200378417969Process finished with exit code 0以上代码相比较于tensorflow1.x来说，较为清爽
- 数据流在网络中的流向，可以像PyTorch那样，直接加断点，查看x在经过各个网络层的前后value和shape的变化
- 有一点我想说明的是，以上代码中的 train_step() 和 test_step() 函数，在pycharm中，不可以通过加断点的方式，进入查看，具体运算情况(比较纳闷)。
- 再有，就是代码中的train_loss,train_accuracy,test_loss, test_accuracy这四个对象，很神奇，我甚至没有看到它们的运算过程，最后竟然可以输出train_loss.result()
关于tensorflow2.0的初体验，就到这里。
目前来看，我还是倾向于PyTorch.
这篇关于关于tensorflow2.0-alpha0版本，尝鲜的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
