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!pip install tensorflow-gpu
import tensorflow as tf print(tf.version)
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from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow import keras
# guide use keras as high-level API to build models
import numpy as np
import matplotlib.pyplot as plt
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fashion_mnist = keras.datasets.fashion_mnist
(train_image, train_labels), (test_image, test_labels) = fashion_mnist.load_data()
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class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
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# eexplore the data
train_image.shape
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len(train_labels)
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test_image.shape
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# preprocess the data
plt.figure()
plt.imshow(train_image[0])
plt.colorbar
plt.grid(False)
plt.show
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train_images = train_image / 255.0
test_images = test_image / 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()
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model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)), # conv2d to flatten array
keras.layers.Dense(128, activation="relu"), # hidden layer
keras.layers.Dense(10, activation="softmax") # output layer
])
# after set up, compile it of loss func, optimizer, metrics(Used to monitor the training and testing steps. )
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# train
model.fit(train_images, train_labels, epochs=10)
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test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest acc: ', test_acc)
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predictions = model.predict(test_images)
print(np.argmax(predictions[0]))
print(test_labels[0])
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# Graph this to look at the full set of 10 class predictions.
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array, 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, true_label[i]
plt.grid(False)
plt.xticks(range(10))
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')
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i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
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i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
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# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and 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[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
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# Grab an image from the test dataset.
img = test_images[1]
print(img.shape)
# Add the image to a batch where it's the only member.
# tf.keras models are optimized to make predictions on a batch, or collection, of examples at once.
# Accordingly, even though you're using a single image, you need to add it to a list:
img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)