Machine Learning Foundations: Exercise 4 Happy and sad image classify model with 99.9% accuracy .

Machine Learning Foundations: Exercise 4 Happy and sad image classify model with 99.9% accuracy:code lab link

Build a mode to classify happy and sad model with convolution neuron network. with more 99.9% accuracy.

 

Note:

•  Model over-fitting: Near 100% accuracy on training data, but lower accuracy on testing data.
•  ImageDataGenerator:  Label data automatic using directory name.

Code: 

	import tensorflow as tf
	import os
	import zipfile
	DESIRED_ACCURACY = 0.999
	# Get the happy-or-sad image data
	!wget --no-check-certificate \
	"https://storage.googleapis.com/laurencemoroney-blog.appspot.com/happy-or-sad.zip" \
	-O "/tmp/happy-or-sad.zip"
	zip_ref = zipfile.ZipFile("/tmp/happy-or-sad.zip", 'r')
	zip_ref.extractall("/tmp/h-or-s")
	zip_ref.close()
	# Callback function to check model accuracy
	class RayCallback(tf.keras.callbacks.Callback):
	def on_epoch_end(self, epoch, logs={}):
	if(logs.get('accuracy')>DESIRED_ACCURACY):
	print("\nReached 99.9% accuracy so cancelling training!")
	self.model.stop_training = True
	callbacks = RayCallback()
	# 5 layer neuron network
	model = tf.keras.models.Sequential([
	tf.keras.layers.Conv2D(16, (3,3), activation='relu',
	input_shape=(300, 300, 3)), # Fit ImageDataGenerator output size
	tf.keras.layers.MaxPooling2D(2, 2),
	tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
	tf.keras.layers.MaxPooling2D(2,2),
	tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(512, activation='relu'),
	tf.keras.layers.Dense(1, activation='sigmoid')
	])
	from tensorflow.keras.optimizers import RMSprop
	model.compile(loss='binary_crossentropy',
	optimizer=RMSprop(lr=0.001),
	metrics=['accuracy'])
	# Label image data base on it's directory name
	from tensorflow.keras.preprocessing.image import ImageDataGenerator
	train_datagen = ImageDataGenerator(1./255)
	train_generator = train_datagen.flow_from_directory(
	'/tmp/h-or-s', # Data directory
	target_size=(300, 300), # Output size
	batch_size=128, # Size of the batches of data, each batch is 8 data since the training data are 80 images
	class_mode='binary' # Two class: Happy, Bad
	)
	# Train the model
	history = model.fit(train_generator,
	steps_per_epoch = 8, # Load 8 data each epoch to parallize the training stage
	epochs=15,
	callbacks=callbacks
	)

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