Resnet_IGG-2.py

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256
train_data_dir = "tf_files/train_data"
validation_data_dir = "tf_files/train_data"
nb_train_samples = 4125
nb_validation_samples = 466 
batch_size = 16
epochs = 50

model = applications.ResNet50(include_top=False, weights='imagenet', input_shape=(img_width, img_height, 3))



# Freeze the layers which you don't want to train. Here I am freezing the all layers.
for layer in model.layers[:]:
    layer.trainable = False

# Adding custom Layer
# We only add
x = model.output
x = Flatten()(x)
# Adding even more custom layers
# x = Dense(1024, activation="relu")(x)
# x = Dropout(0.5)(x)
# x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation="softmax")(x)

# creating the final model 
model_final = Model(input = model.input, output = predictions)

# compile the model 
model_final.compile(loss = "categorical_crossentropy", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"])
# feel free to try out the adam optimizer as well

# Initiate the train and test generators with data Augumentation 
train_datagen = ImageDataGenerator(
  rescale = 1./255,
  horizontal_flip = True,
  fill_mode = "nearest",
  zoom_range = 0.3,
  width_shift_range = 0.3,
  height_shift_range=0.3,
  rotation_range=30)

test_datagen = ImageDataGenerator(
  rescale = 1./255,
  horizontal_flip = True,
  fill_mode = "nearest",
  zoom_range = 0.3,
  width_shift_range = 0.3,
  height_shift_range=0.3,
  rotation_range=30)

train_generator = train_datagen.flow_from_directory(
  train_data_dir,
  target_size = (img_height, img_width),
  batch_size = batch_size,
  class_mode = "categorical")

validation_generator = test_datagen.flow_from_directory(
  validation_data_dir,
  target_size = (img_height, img_width),
  class_mode = "categorical")

# Save the model according to the conditions  
checkpoint = ModelCheckpoint("resnet50_retrain.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')


# Train the model 
model_final.fit_generator(
  train_generator,
  samples_per_epoch = nb_train_samples,
  epochs = epochs,
  validation_data = validation_generator,
  nb_val_samples = nb_validation_samples,
  callbacks = [checkpoint, early])