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03-train_cnn.py
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03-train_cnn.py
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import json
import os
from distutils.dir_util import copy_tree
import shutil
import pandas as pd
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow.keras import backend as K
print('TensorFlow version: ', tf.__version__)
dataset_path = '.\\split_dataset\\'
tmp_debug_path = '.\\tmp_debug'
print('Creating Directory: ' + tmp_debug_path)
os.makedirs(tmp_debug_path, exist_ok=True)
def get_filename_only(file_path):
file_basename = os.path.basename(file_path)
filename_only = file_basename.split('.')[0]
return filename_only
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import applications
from efficientnet.tfkeras import EfficientNetB0 #EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.models import load_model
input_size = 128
batch_size_num = 32
train_path = os.path.join(dataset_path, 'train')
val_path = os.path.join(dataset_path, 'val')
test_path = os.path.join(dataset_path, 'test')
train_datagen = ImageDataGenerator(
rescale = 1/255, #rescale the tensor values to [0,1]
rotation_range = 10,
width_shift_range = 0.1,
height_shift_range = 0.1,
shear_range = 0.2,
zoom_range = 0.1,
horizontal_flip = True,
fill_mode = 'nearest'
)
train_generator = train_datagen.flow_from_directory(
directory = train_path,
target_size = (input_size, input_size),
color_mode = "rgb",
class_mode = "binary", #"categorical", "binary", "sparse", "input"
batch_size = batch_size_num,
shuffle = True
#save_to_dir = tmp_debug_path
)
val_datagen = ImageDataGenerator(
rescale = 1/255 #rescale the tensor values to [0,1]
)
val_generator = val_datagen.flow_from_directory(
directory = val_path,
target_size = (input_size, input_size),
color_mode = "rgb",
class_mode = "binary", #"categorical", "binary", "sparse", "input"
batch_size = batch_size_num,
shuffle = True
#save_to_dir = tmp_debug_path
)
test_datagen = ImageDataGenerator(
rescale = 1/255 #rescale the tensor values to [0,1]
)
test_generator = test_datagen.flow_from_directory(
directory = test_path,
classes=['real', 'fake'],
target_size = (input_size, input_size),
color_mode = "rgb",
class_mode = None,
batch_size = 1,
shuffle = False
)
# Train a CNN classifier
efficient_net = EfficientNetB0(
weights = 'imagenet',
input_shape = (input_size, input_size, 3),
include_top = False,
pooling = 'max'
)
model = Sequential()
model.add(efficient_net)
model.add(Dense(units = 512, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))
model.summary()
# Compile model
model.compile(optimizer = Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
checkpoint_filepath = '.\\tmp_checkpoint'
print('Creating Directory: ' + checkpoint_filepath)
os.makedirs(checkpoint_filepath, exist_ok=True)
custom_callbacks = [
EarlyStopping(
monitor = 'val_loss',
mode = 'min',
patience = 5,
verbose = 1
),
ModelCheckpoint(
filepath = os.path.join(checkpoint_filepath, 'best_model.h5'),
monitor = 'val_loss',
mode = 'min',
verbose = 1,
save_best_only = True
)
]
# Train network
num_epochs = 20
history = model.fit_generator(
train_generator,
epochs = num_epochs,
steps_per_epoch = len(train_generator),
validation_data = val_generator,
validation_steps = len(val_generator),
callbacks = custom_callbacks
)
print(history.history)
'''
# Plot results
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label = 'Training Accuracy')
plt.plot(epochs, val_acc, 'b', label = 'Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label = 'Training loss')
plt.plot(epochs, val_loss, 'b', label = 'Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()
'''