[CNN] Day 03_vision02_Model_Transfer_With_Augmentation

# -*- coding: utf-8 -*-
"""ML_Day03_vision04_ModelTransfer.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1u0HQZajD-qHwdDzMVvkpMd2_Yg9wNoND
"""
 
from google.colab import drive
drive.mount('/gdrive')
 
PATH = "/gdrive/My Drive/Colab Notebooks/resources/"
 
import matplotlib.pyplot as plt
from tensorflow import keras
import tensorflow as tf
import numpy as np
 
print(tf.__version__)
 
import os
 
## import layer to handle which layer to be taken
from tensorflow.keras import layers
from tensorflow.keras import Model
 
!wget --no-check-certificate \
    https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
    -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
 
## import model skelton
from tensorflow.keras.applications.inception_v3 import InceptionV3
 
## load weights
local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
 
## setup model skelton
## make include_top to false, because there are fully connected dense layers
pre_trained_model = InceptionV3(input_shape = (150, 150, 3), 
                                include_top = False, 
                                weights = None)
 
## fit weights to the model skeleton
pre_trained_model.load_weights(local_weights_file)
 
## turn trainable option to false to lock upper layers in pretrained model
for layer in pre_trained_model.layers:
  layer.trainable = False
  
pre_trained_model.summary()
 
## cut from certain layer to generalize model
last_layer = pre_trained_model.get_layer('mixed7')
 
## check shape of last layer from which we cut
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
 
from tensorflow.keras.optimizers import RMSprop
 
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
 
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
 
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)                  
 
# Add a final sigmoid layer for classification
x = layers.Dense  (1, activation='sigmoid')(x)           
 
# Add our new layer to pretrained model
model = Model( pre_trained_model.input, x)
 
# specify optimizer and loss func
model.compile(optimizer = RMSprop(lr=0.0001), 
              loss = 'binary_crossentropy', 
              metrics = ['acc'])
 
## download dog vs cat dataset
!wget --no-check-certificate \
        https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \
       -O /tmp/cats_and_dogs_filtered.zip
 
    
from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
import os
import zipfile
 
local_zip = '//tmp/cats_and_dogs_filtered.zip'
 
zip_ref = zipfile.ZipFile(local_zip, 'r')
 
zip_ref.extractall('/tmp')
zip_ref.close()
 
# Define our example directories and files
base_dir = '/tmp/cats_and_dogs_filtered'
 
train_dir = os.path.join( base_dir, 'train')
validation_dir = os.path.join( base_dir, 'validation')
 
 
train_cats_dir = os.path.join(train_dir, 'cats') # Directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs') # Directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats') # Directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')# Directory with our validation dog pictures
 
train_cat_fnames = os.listdir(train_cats_dir)
train_dog_fnames = os.listdir(train_dogs_dir)
 
# Add our data-augmentation parameters to ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255.,
                                   rotation_range = 40,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
 
 
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator( rescale = 1.0/255. )
 
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(train_dir,
                                                    batch_size = 20,
                                                    class_mode = 'binary', 
                                                    target_size = (150, 150))     
 
 
# Flow validation images in batches of 20 using test_datagen generator
validation_generator =  test_datagen.flow_from_directory( validation_dir,
                                                          batch_size  = 20,
                                                          class_mode  = 'binary', 
                                                          target_size = (150, 150))
 
history = model.fit_generator(
            train_generator,
            validation_data = validation_generator,
            steps_per_epoch = 100,
            epochs = 20,
            validation_steps = 50,
            verbose = 2)
 
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(len(acc))
 
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()
 
 
plt.show()
 

참고자료 및 출처 : Convolutional Neural Networks in TensorFlow - Weak 3 Exercise