03-train_model.py

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 tf.keras import backend as K
import keras
from keras import backend as K
print('TensorFlow version: ', tf.__version__)

# Set to force CPU
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
#if tf.test.gpu_device_name():
#    print('GPU found')
#else:
#    print("No GPU found")

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 = tf.keras.preprocessing.image.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 = tf.keras.preprocessing.image.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 = tf.keras.preprocessing.image.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 = tf.keras.models.Sequential()
model.add(efficient_net)
model.add(tf.keras.layers.Dense(units = 512, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(units = 128, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)))
model.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))
model.summary()

# Compile model
model.compile(optimizer = tf.keras.optimizers.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 = [
    tf.keras.callbacks.EarlyStopping(
        monitor = 'val_loss',
        mode = 'min',
        patience = 5,
        verbose = 1
    ),
    tf.keras.callbacks.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 = 25
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['accuracy']
val_acc = history.history['val_accuracy']
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()

# load the saved model that is considered the best
best_model = tf.keras.models.load_model(os.path.join(checkpoint_filepath, 'best_model.h5'))

# Generate predictions
test_generator.reset()

preds = best_model.predict(
    test_generator,
    verbose = 1
)

test_results = pd.DataFrame({
    "Filename": test_generator.filenames,
    "Prediction": preds.flatten()
})
print(test_results)



# 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__)

# # Set to force CPU
# #os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# #if tf.test.gpu_device_name():
# #    print('GPU found')
# #else:
# #    print("No GPU found")

# 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()
# '''

# # load the saved model that is considered the best
# best_model = load_model(os.path.join(checkpoint_filepath, 'best_model.h5'))

# # Generate predictions
# test_generator.reset()

# preds = best_model.predict(
#     test_generator,
#     verbose = 1
# )

# test_results = pd.DataFrame({
#     "Filename": test_generator.filenames,
#     "Prediction": preds.flatten()
# })
# print(test_results)