Learn image recognition with fashion_mnist

mnist-fashion-example.py

# Based on https://www.coursera.org/learn/introduction-tensorflow/home/welcome
# Course 1 - Part 4 - Lesson 2 - Notebook

#https://github.com/zalandoresearch/fashion-mnist

import tensorflow as tf
from tensorflow import keras
mnist = keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
import matplotlib.pyplot as plt

#plt.imshow(training_images[42])
#print(training_labels[42])
#print(training_images[42])
training_images = training_images / 255.0
test_images = test_images / 255.0
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
                                 tf.keras.layers.Dense(128, activation=tf.nn.relu), #128 neurones
                                 tf.keras.layers.Dense(10, activation=tf.nn.softmax)]) #10 classes
model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(training_images, training_labels, epochs=5)

model.evaluate(test_images, test_labels)

classifications = model.predict(test_images)

classes = {
0:	"T-shirt/top",
1:	"Trouser",
2:	"Pullover",
3:	"Dress",
4:	"Coat",
5:	"Sandal",
6:	"Shirt",
7:	"Sneaker",
8:	"Bag",
9:	"Ankle boot"
}

show_items = 10
_, axarr = plt.subplots(1,show_items)
for index in range(show_items):
  axarr[index].imshow(test_images[index])
  #print(classifications[index])
  #print(test_labels[index])
  predictedClass=classifications.argmax(axis=1)[index]
  print(classes[predictedClass])