TensorFlow 2.0 教程，這節開始是深度學習實踐
1.獲取Fashion MNIST數據集
本指南使用Fashion MNIST數據集，該數據集包含10個類別中的70,000個灰度圖像。 圖像顯示了低分辨率(28 x 28像素)的單件服裝，如下所示：
Fashion MNIST旨在替代經典的MNIST數據集，通常用作計算機視覺機器學習計划的“Hello，World”。
我們將使用60,000張圖像來訓練網絡和10,000張圖像，以評估網絡學習圖像分類的准確程度。
(train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()
圖像是28x28 NumPy數組，像素值介於0到255之間。標簽是一個整數數組，范圍從0到9.這些對應於圖像所代表的服裝類別：
Label Class
0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot
每個圖像都映射到一個標簽。 由於類名不包含在數據集中，因此將它們存儲在此處以便在繪制圖像時使用：
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
2.探索數據
讓我們在訓練模型之前探索數據集的格式。 以下顯示訓練集中有60,000個圖像，每個圖像表示為28 x 28像素：
print(train_images.shape)
print(train_labels.shape)
print(test_images.shape)
print(test_labels.shape)
(60000, 28, 28)
(60000,)
(10000, 28, 28)
(10000,)
3.處理數據
圖片展示
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
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]])
plt.show()
4.構造網絡
model = keras.Sequential(
[
layers.Flatten(input_shape=[28, 28]),
layers.Dense(128, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
5.訓練與驗證
model.fit(train_images, train_labels, epochs=5)
Epoch 1/5
60000/60000 [==============================] - 3s 58us/sample - loss: 0.4970 - accuracy: 0.8264
Epoch 2/5
60000/60000 [==============================] - 3s 43us/sample - loss: 0.3766 - accuracy: 0.8651
Epoch 3/5
60000/60000 [==============================] - 3s 42us/sample - loss: 0.3370 - accuracy: 0.8777
Epoch 4/5
60000/60000 [==============================] - 3s 42us/sample - loss: 0.3122 - accuracy: 0.8859
Epoch 5/5
60000/60000 [==============================] - 3s 42us/sample - loss: 0.2949 - accuracy: 0.8921
model.evaluate(test_images, test_labels)
[0.3623474566936493, 0.8737]
6.預測
predictions = model.predict(test_images)
print(predictions[0])
print(np.argmax(predictions[0]))
print(test_labels[0])
[2.1831402e-05 1.0357383e-06 1.0550731e-06 1.3231372e-06 8.0873624e-06
2.6805745e-02 1.2466960e-05 1.6174167e-01 1.4259206e-04 8.1126428e-01]
9
9
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: 無錫人流多少錢 http://www.xaytsgyy.com/
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')
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)
plt.show()
# 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_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions, test_labels)
plt.show()
img = test_images[0]
img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
plot_value_array(0, predictions_single, test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
(1, 28, 28)
[[2.1831380e-05 1.0357381e-06 1.0550700e-06 1.3231397e-06 8.0873460e-06
2.6805779e-02 1.2466959e-05 1.6174166e-01 1.4259205e-04 8.1126422e-01]]
