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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']
train_images = train_images/255.0
test_images = test_images/255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
model.fit(train_images, train_labels, epochs=10)
predictions = model.predict(test_images)
for i in range(5):
plt.grid(False)
plt.imshow(test_images[i], cmap=plt.cm.binary)
plt.xlabel("Actual:"+class_names[test_labels[i]])
plt.title("Prediction:"+class_names[np.argmax(predictions[i])])
plt.show()
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