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- from tensorflow.keras.models import Sequential
- from tensorflow.keras.layers import Convolution2D
- from tensorflow.keras.layers import MaxPooling2D
- from tensorflow.keras.layers import Flatten
- from tensorflow.keras.layers import Dense
- classifier = Sequential()
- classifier.add(Convolution2D(32,3,3, input_shape = (64,64,3), activation = 'relu'))
- classifier.add(MaxPooling2D(pool_size = (2,2)))
- classifier.add(Flatten())
- classifier.add(Dense(128, activation = 'relu')) #output_dim =
- classifier.add(Dense( 1, activation = 'sigmoid'))
- classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
- from tensorflow.keras.preprocessing.image import ImageDataGenerator
- train_datagen = ImageDataGenerator(
- rescale=1./255,
- shear_range=0.2,
- zoom_range=0.2,
- horizontal_flip=True)
- test_datagen = ImageDataGenerator(rescale=1./255)
- training_set = train_datagen.flow_from_directory(
- './train',
- target_size=(64,64),
- batch_size=32,
- class_mode='binary')
- test_set = train_datagen.flow_from_directory(
- './test',
- target_size=(64,64),
- batch_size=32,
- class_mode='binary')
- from IPython.display import display
- from PIL import Image
- classifier.fit_generator(
- training_set,
- steps_per_epoch=36,
- epochs=3,
- validation_data=test_set,
- validation_steps=5)
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