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continue_training_NasNet.py
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continue_training_NasNet.py
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import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import ModelCheckpoint
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
train_path=r'c:\Image Recognition tenserflow\train2'
model_name = 'train2_NASNetLarge_continue.h5'
save_model_name = 'train2_NASNetLarge_continue2.h5'
#auto_save_model_name = 'train2_NASNetLarge_continue_auto.h5'
image_size = 331 # All images will be resized to
batch_size = 16
# Rescale all images by 1./255 and apply image augmentation
train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255,
validation_split=0.2,
rotation_range=5,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
shear_range=0.2,
zoom_range=0.2)
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
train_path, # Source directory for the training images
target_size=(image_size, image_size),
batch_size=batch_size,
class_mode='categorical',
subset='training')
# Flow validation images in batches of 20 using test_datagen generator
validation_generator = train_datagen.flow_from_directory(
train_path, # Source directory for the validation images
target_size=(image_size, image_size),
batch_size=batch_size,
class_mode='categorical',
subset='validation')
model = tf.keras.models.load_model(model_name)
#if os.path.exists(auto_save_model_name):
# model.load_weights(auto_save_model_name)
# print ("Checkpoint '" + auto_save_model_name + "' loaded.")
"""### Train the model
After training for 10 epochs, we are able to get ~94% accuracy.
If you have more time, train it to convergence (50 epochs, ~96% accuracy)
"""
epochs = 10
steps_per_epoch = train_generator.n // batch_size
validation_steps = validation_generator.n // batch_size
#Save the model after every epoch.
#mc_fit = ModelCheckpoint(auto_save_model_name, monitor='val_acc', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', save_freq=1)
history = model.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
workers=4,
validation_data=validation_generator,
validation_steps=validation_steps)
# save model and architecture to single file
model.save(save_model_name)
print("Saved model to disk")
"""### Learning curves
Let's take a look at the learning curves of the training and validation accuracy / loss, when using the MobileNet V2 base model as a fixed feature extractor.
If you train to convergence (`epochs=50`) the resulting graph should look like this:
"""
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')
plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,max(plt.ylim())])
plt.title('Training and Validation Loss')
plt.show()