基于tensorflow_gpu 1.9.0实现的第一个神经网络：简单图像分类

示例来源于Tensorflow的官方教程。
基于tensorflow_gpu 1.9.0实现的第一个神经网络：简单图像分类，代码如下：

#!/usr/bin/env python
import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
                               test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# Preprocess the data.
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.gca().grid(False)
plt.title('The first image in the training set')

train_images = train_images / 255.0
test_images = test_images / 255.0

# Display the first 25 images from the training set
plt.figure(figsize=(10, 10))
for i in range(25):    
    plt.subplot(5, 5, i+1)    
    plt.xticks([])
    plt.yticks([])
    plt.grid('off')
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.suptitle('The first 25 images from the training set')

# Setup the layers
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

# Compile the model
model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=5)

# Evaluate accuracy
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy: ', test_acc)

# Make predictions
predictions = model.predict(test_images)

# Plot the first 25 test images, their predicted label, and the true label
# Color correct predictions in green, incorrect predictions in red
plt.figure(figsize=(10, 10))
for i in range(25):    
    plt.subplot(5, 5, i+1)       
    plt.xticks([])
    plt.yticks([])
    plt.grid('off')
    plt.imshow(test_images[i], cmap=plt.cm.binary)
    predicted_label = np.argmax(predictions[i])
    true_label = test_labels[i]
    if predicted_label == true_label:
        color = 'green'
    else:
        color = 'red'
    plt.xlabel("{} ({})".format(
        class_names[predicted_label],
        class_names[true_label]),
        color=color)
plt.suptitle('The first 25 images from the test set') 

# Show figures.
plt.show()

运行该程序之前，需要先根据我的另一篇博客《Ubuntu 16.04安装tensorflow_gpu 1.9.0的方法》，安装tensorflow_gpu 1.9.0，然后安装numpy、matplotlib等工具包，此外还需要安装python-tk，命令如下：

sudo pip install numpy
sudo pip install matplotlib
sudo apt-get install python-tk

运行结果如下：