Coursera Tensorflow Developer Professional Certificate - cnn in tensorflow week03 (transfer-learning)
Tags: cnn, coursera-tensorflow-developer-professional-certificate, tensorflow, transfer-learning
Transfer Learning
NoteBook
import os
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
from tensorflow.keras.applications.inception_v3 import InceptionsV3
local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
pre_trained_model = InceptionsV3( input_shape=(150, 150, 3),
include_top=False,
weights=None)
pre_trained_model.load_weights(local_weights_file)
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
# last layer output shape: (None, 7, 7, 768)
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)
# Addd a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer = RMSprop(lr=0.0001),
loss = 'binary_crossentropy',
metrics = ['accuracy'])
!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_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')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
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)
test_datagen = ImageDataGenerator( rescale = 1./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(
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('accuracy')
val_acc history.history['val_accuracy']
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()
Droupout
我還記得 第一次調
超餐 dropout時候的感動拉～～～ 超酷得兒！！！
Week 3 Quiz (100 分拉！)
-
If I put a dropout parameter of 0.2, how many nodes will I lose?
- 20% of them
-
Why is transfer learning useful?
- Because I can use the features that were learned from large datasets that I may not have access to
-
How did you lock or freeze a layer from retraining?
- layer.trainable = false
-
How do you change the number of classes the model can classify when using transfer learning? (i.e. the original model handled 1000 classes, but yours handles just 2)
- When you add your DNN at the bottom of the network, you specify your output layer with the number of classes you want
-
Can you use Image Augmentation with Transfer Learning Models?
- Yes, because you are adding new layers at the bottom of the network, and you can use image augmentation when training these
-
Why do dropouts help avoid overfitting?
- Because neighbor neurons can have similar weights, and thus can skew the final training
-
What would the symptom of a Dropout rate being set too high?
- The network would lose specialization to the effect that it would be inefficient or ineffective at learning, driving accuracy down
-
Which is the correct line of code for adding Dropout of 20% of neurons using TensorFlow
- tf.keras.layers.Dropout(0.2),
Exercise_3_Horses_vs_humans_using_Transfer_Learning_Question-FINAL
# ATTENTION: Please do not alter any of the provided code in the exercise. Only add your own code where indicated
# ATTENTION: Please do not add or remove any cells in the exercise. The grader will check specific cells based on the cell position.
# ATTENTION: Please use the provided epoch values when training.
# Import all the necessary files!
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from os import getcwd
path_inception = f"{getcwd()}/../tmp2/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
# Import the inception model
from tensorflow.keras.applications.inception_v3 import InceptionV3
# Create an instance of the inception model from the local pre-trained weights
local_weights_file = path_inception
pre_trained_model = InceptionV3( input_shape=(150, 150, 3),
include_top= False,
weights= None)
pre_trained_model.load_weights(local_weights_file)
# Make all the layers in the pre-trained model non-trainable
for layer in pre_trained_model.layers:
# Your Code Here
layer.trainable = False
# Print the model summary
pre_trained_model.summary()
# Expected Output is extremely large, but should end with:
#batch_normalization_v1_281 (Bat (None, 3, 3, 192) 576 conv2d_281[0][0]
#__________________________________________________________________________________________________
#activation_273 (Activation) (None, 3, 3, 320) 0 batch_normalization_v1_273[0][0]
#__________________________________________________________________________________________________
#mixed9_1 (Concatenate) (None, 3, 3, 768) 0 activation_275[0][0]
# activation_276[0][0]
#__________________________________________________________________________________________________
#concatenate_5 (Concatenate) (None, 3, 3, 768) 0 activation_279[0][0]
# activation_280[0][0]
#__________________________________________________________________________________________________
#activation_281 (Activation) (None, 3, 3, 192) 0 batch_normalization_v1_281[0][0]
#__________________________________________________________________________________________________
#mixed10 (Concatenate) (None, 3, 3, 2048) 0 activation_273[0][0]
# mixed9_1[0][0]
# concatenate_5[0][0]
# activation_281[0][0]
#==================================================================================================
#Total params: 21,802,784
#Trainable params: 0
#Non-trainable params: 21,802,784
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
# Expected Output:
# ('last layer output shape: ', (None, 7, 7, 768))
# Define a Callback class that stops training once accuracy reaches 97.0%
class myCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
if(logs.get('acc')>0.97):
print("\nReached 97.0% accuracy so cancelling training!")
self.model.stop_training = True
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)# Your Code Here)(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)# Your Code Here)(x)
# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer = RMSprop(lr=0.0001),
loss = 'binary_crossentropy',
metrics = ['accuracy'])
model.summary()
# Expected output will be large. Last few lines should be:
# mixed7 (Concatenate) (None, 7, 7, 768) 0 activation_248[0][0]
# activation_251[0][0]
# activation_256[0][0]
# activation_257[0][0]
# __________________________________________________________________________________________________
# flatten_4 (Flatten) (None, 37632) 0 mixed7[0][0]
# __________________________________________________________________________________________________
# dense_8 (Dense) (None, 1024) 38536192 flatten_4[0][0]
# __________________________________________________________________________________________________
# dropout_4 (Dropout) (None, 1024) 0 dense_8[0][0]
# __________________________________________________________________________________________________
# dense_9 (Dense) (None, 1) 1025 dropout_4[0][0]
# ==================================================================================================
# Total params: 47,512,481
# Trainable params: 38,537,217
# Non-trainable params: 8,975,264
# Get the Horse or Human dataset
path_horse_or_human = f"{getcwd()}/../tmp2/horse-or-human.zip"
# Get the Horse or Human Validation dataset
path_validation_horse_or_human = f"{getcwd()}/../tmp2/validation-horse-or-human.zip"
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import zipfile
import shutil
shutil.rmtree('/tmp')
local_zip = path_horse_or_human
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/training')
zip_ref.close()
local_zip = path_validation_horse_or_human
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp/validation')
zip_ref.close()
# Define our example directories and files
train_dir = '/tmp/training'
validation_dir = '/tmp/validation'
train_horses_dir = os.path.join(train_dir, 'horses')
train_humans_dir = os.path.join(train_dir, 'humans')
validation_horses_dir = os.path.join(validation_dir, 'horses')
validation_humans_dir = os.path.join(validation_dir, 'humans')
train_horses_fnames = os.listdir(train_horses_dir)
train_humans_fnames = os.listdir(train_humans_dir)
validation_horses_fnames = os.listdir(validation_horses_dir)
validation_humans_fnames = os.listdir(validation_humans_dir)
print(len(train_horses_fnames))
print(len(train_humans_fnames))
print(len(validation_horses_fnames))
print(len(validation_humans_fnames))
# Expected Output:
# 500
# 527
# 128
# 128
# Add our data-augmentation parameters to ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.3,
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./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))
# Expected Output:
# Found 1027 images belonging to 2 classes.
# Found 256 images belonging to 2 classes.
# Run this and see how many epochs it should take before the callback
# fires, and stops training at 97% accuracy
callbacks = myCallback
history = model.fit_generator(train_generator,
validation_data = validation_generator,
steps_per_epoch = 100,
epochs = 3,
validation_steps = 50,
verbose = 2)
# Your Code Here (set epochs = 3))
%matplotlib inline
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(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()
