/
file.py
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file.py
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from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.models import Sequential
model = Sequential()
model.add(Convolution2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.summary()
model.add(Flatten())
model.summary()
model.add(Dense(units=128, activation='relu'))
model.summary()
model.add(Dense(units=1, activation='sigmoid'))
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
from 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(
'/dataset/cnn_dataset/training_set/',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'/dataset/cnn_dataset/test_set/',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
history=model.fit(
training_set,
steps_per_epoch=8000,
epochs=1,
validation_data=test_set,
validation_steps=2000)
model.save('mymodel.h5')
from keras.preprocessing import image
test_image = image.load_img('/dataset/cnn_dataset/single_prediction/cat_or_dog_2.jpg',target_size=(64,64))
test_image = image.img_to_array(test_image)
import numpy as np
test_image = np.expand_dims(test_image, axis=0)
result = model.predict(test_image)
accuracy=model.evaluate_generator(test_set)
with open('acc_file.txt','w') as f:
f.write(str(accuracy[1]))