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October 21, 2019 08:19
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TensorFlow CNN model training example
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# TensorFlow CNN model training example | |
# based on https://www.tensorflow.org/tutorials/images/cnn | |
from __future__ import absolute_import, division, print_function, unicode_literals | |
import tensorflow as tf | |
from tensorflow.keras import datasets, layers, models | |
import matplotlib.pyplot as plt | |
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() | |
# Normalize pixel values to be between 0 and 1 | |
train_images, test_images = train_images / 255.0, test_images / 255.0 | |
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', | |
'dog', 'frog', 'horse', 'ship', 'truck'] | |
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) | |
# The CIFAR labels happen to be arrays, | |
# which is why you need the extra index | |
plt.xlabel(class_names[train_labels[i][0]]) | |
plt.show() | |
model = models.Sequential() | |
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
model.add(layers.Flatten()) | |
model.add(layers.Dense(64, activation='relu')) | |
model.add(layers.Dense(10, activation='softmax')) | |
model.summary() | |
model.compile(optimizer='adam', | |
loss='sparse_categorical_crossentropy', | |
metrics=['accuracy']) | |
history = model.fit(train_images, train_labels, epochs=10, | |
validation_data=(test_images, test_labels)) | |
plt.plot(history.history['acc'], label='accuracy') | |
plt.plot(history.history['val_acc'], label = 'val_accuracy') | |
plt.xlabel('Epoch') | |
plt.ylabel('Accuracy') | |
plt.ylim([0.5, 1]) | |
plt.legend(loc='lower right') | |
plt.show() | |
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) | |
print(test_acc) | |
model.save('my_model.h5') |
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