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- import os
- from keras.preprocessing import image
- import matplotlib.pyplot as plt
- import numpy as np
- from keras.utils.np_utils import to_categorical
- import random,shutil
- from keras.models import Sequential
- from keras.layers import Dropout,Conv2D,Flatten,Dense, MaxPooling2D, BatchNormalization
- from keras.models import load_model
- def generator(dir, gen=image.ImageDataGenerator(rescale=1./255), shuffle=True,batch_size=1,target_size=(24,24),class_mode='categorical' ):
- return gen.flow_from_directory(dir,batch_size=batch_size,shuffle=shuffle,color_mode='grayscale',class_mode=class_mode,target_size=target_size)
- BS= 32
- TS=(24,24)
- train_batch= generator('data/train',shuffle=True, batch_size=BS,target_size=TS)
- valid_batch= generator('data/valid',shuffle=True, batch_size=BS,target_size=TS)
- SPE= len(train_batch.classes)//BS
- VS = len(valid_batch.classes)//BS
- print(SPE,VS)
- # img,labels= next(train_batch)
- # print(img.shape)
- model = Sequential([
- Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(24,24,1)),
- MaxPooling2D(pool_size=(1,1)),
- Conv2D(32,(3,3),activation='relu'),
- MaxPooling2D(pool_size=(1,1)),
- #32 convolution filters used each of size 3x3
- #again
- Conv2D(64, (3, 3), activation='relu'),
- MaxPooling2D(pool_size=(1,1)),
- #64 convolution filters used each of size 3x3
- #choose the best features via pooling
-
- #randomly turn neurons on and off to improve convergence
- Dropout(0.25),
- #flatten since too many dimensions, we only want a classification output
- Flatten(),
- #fully connected to get all relevant data
- Dense(128, activation='relu'),
- #one more dropout for convergence' sake :)
- Dropout(0.5),
- #output a softmax to squash the matrix into output probabilities
- Dense(2, activation='softmax')
- ])
- model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
- model.fit_generator(train_batch, validation_data=valid_batch,epochs=15,steps_per_epoch=SPE ,validation_steps=VS)
- model.save('models/cnnCat2.h5', overwrite=True)
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