minst.py

import matplotlib.pyplot as plt

import tensorflow as tf
layers = tf.keras.layers

import numpy as np

print(tf.__version__)

mnist = tf.keras.datasets.fashion_mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, t_test = x_train / 255.0, x_test / 255.0

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
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(x_train[i], cmap=plt.cm.binary)
 plt.xlabel(class_names[y_train[i]])
plt.show()

model = tf.keras.Sequential()
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test, y_test)

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')

predictions = model.predict(x_test)
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, y_test, x_test)
plt.subplot(1,2,2)
plot_value_array(i, predictions, y_test)
plt.show()