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dogs_vs_cats.py
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dogs_vs_cats.py
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import os, shutil
orignal_dataset_dir=(r"C:\Users\sbans\Desktop\New folder\train1")
base_dir=(r"C:\Users\sbans\Desktop\cats_and_dogs")
os.mkdir(base_dir)
train_dir=os.path.join(base_dir,'train')
os.mkdir(train_dir)
validation_dir=os.path.join(base_dir,'validation')
os.mkdir(validation_dir)
test_dir=os.path.join(base_dir,'test')
os.mkdir(test_dir)
train_cats_dir=os.path.join(train_dir,'cats')
os.mkdir(train_cats_dir)
train_dogs_dir=os.path.join(train_dir,'dogs')
os.mkdir(train_dogs_dir)
validation_cats_dir=os.path.join(validation_dir,'cats')
os.mkdir(validation_cats_dir)
validation_dogs_dir=os.path.join(validation_dir,'dogs')
os.mkdir(validation_dogs_dir)
test_cats_dir=os.path.join(test_dir,'cats')
os.mkdir(test_cats_dir)
test_dogs_dir=os.path.join(test_dir,'dogs')
os.mkdir(test_dogs_dir)
fnames=['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(train_cats_dir,fname)
shutil.copyfile(src,dst)
fnames=['cat.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(validation_cats_dir,fname)
shutil.copyfile(src,dst)
fnames=['cat.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(test_cats_dir,fname)
shutil.copyfile(src,dst)
fnames=['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(train_dogs_dir,fname)
shutil.copyfile(src,dst)
fnames=['dog.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(validation_dogs_dir,fname)
shutil.copyfile(src,dst)
fnames=['dog.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:
src=os.path.join(orignal_dataset_dir,fname)
dst=os.path.join(test_dogs_dir,fname)
shutil.copyfile(src,dst)
import tensorflow as tf
with tf.device('/gpu:0'):
from keras import layers
from keras import models
from keras import optimizers
model=models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])
from keras.preprocessing.image import 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)
train_generator=train_datagen.flow_from_directory(train_dir,target_size=(150,150),batch_size=32,class_mode='binary')
validation_generator=test_datagen.flow_from_directory(validation_dir,target_size=(150,150),batch_size=32,class_mode='binary')
history=model.fit_generator(train_generator,steps_per_epoch=100,epochs=100,validation_data=validation_generator,validation_steps=50)
model.save('cats_and_dogs_small.h5')
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(1,len(acc)+1)
plt.plot(epochs,acc,'bo',label='Trainning acc')
plt.plot(epochs,val_acc,'b',label='Validation acc')
plt.title('Training and Validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs,loss,'bo',label='Training loss')
plt.plot(epochs,val_loss,'b',label='Validation loss')
plt.title('Training and Validation Loss')
plt.show()