python学习日记16 keras 进行Fashion MNIST分类

keras官网
https://keras.io/
fashion-mnist文档
https://github.com/zalandoresearch/fashion-mnist
tensorflow文档
https://tensorflow.google.cn/tutorials/keras/basic_classification

代码
其中load_data模块看日记15
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

import load_data

def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])

plt.imshow(img, cmap=plt.cm.binary)

predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = ‘blue’
else:
color = ‘red’

plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)

def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)

thisplot[predicted_label].set_color(‘red’)
thisplot[true_label].set_color(‘blue’)

#print(tf.version)

tf.reset_default_graph()

#fashion_mnist = keras.datasets.fashion_mnist
#(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

(train_images, train_labels), (test_images, test_labels) = load_data.load_data()

class_names = [‘T-shirt/top’, ‘Trouser’, ‘Pullover’, ‘Dress’, ‘Coat’,
‘Sandal’, ‘Shirt’, ‘Sneaker’, ‘Bag’, ‘Ankle boot’]

print(‘train image shape:’,train_images.shape)
print(‘train labels length’,len(train_labels))

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)

train_images = train_images/255.0
test_images = test_images/255.0

plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])

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)])

model.compile(optimizer=tf.train.AdamOptimizer(),
loss=‘sparse_categorical_crossentropy’,
metrics=[‘accuracy’])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)

print(‘Test accuracy:’, test_acc)

predictions = model.predict(test_images)
#if np.argmax(predictions[0]) ==test_labels[0] it is correct

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)

#####Plot the first X test images, their predicted label, and the true label
####Color correct predictions in blue, incorrect predictions in red
num_rows = 5
num_cols = 3
num_images = num_rowsnum_cols
plt.figure(figsize=(22num_cols, 2num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2num_cols, 2i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2num_cols, 2i+2)
plot_value_array(i, predictions, test_labels)

###Grab an image from the test dataset
img = test_images[0]
#Add the image to a batch where it’s the only member.
img = (np.expand_dims(img,0))
predictions_single = model.predict(img)
plt.figure(figsize=(5,5))
plot_value_array(0, predictions_single, test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)