LearnToRecycle/model.py at master · sarmijavier/LearnToRecycle · GitHub

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import numpy as np
import cv2
from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.layers import Conv2D, Flatten, MaxPooling2D,Dense,Dropout,SpatialDropout2D
from keras.models  import Sequential
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img, array_to_img
import random,os,glob
import matplotlib.pyplot as plt


dir_path = 'Path of the dataset'

img_list = glob.glob(os.path.join(dir_path, '*/*.jpg'))

print(len(img_list))


# Image Pre Processing
train=ImageDataGenerator(horizontal_flip=True,
                         vertical_flip=True,
                         validation_split=0.1,
                         rescale=1./255,
                         shear_range = 0.1,
                         zoom_range = 0.1,
                         width_shift_range = 0.1,
                         height_shift_range = 0.1,)

test=ImageDataGenerator(rescale=1/255,
                        validation_split=0.1)

train_generator=train.flow_from_directory(dir_path,
                                          target_size=(300,300),
                                          batch_size=32,
                                          class_mode='categorical',
                                          subset='training')

test_generator=test.flow_from_directory(dir_path,
                                        target_size=(300,300),
                                        batch_size=32,
                                        class_mode='categorical',
                                        subset='validation')


labels = (train_generator.class_indices)
print(labels)

labels = dict((v,k) for k,v in labels.items())
print(labels)

for image_batch, label_batch in train_generator:
  break
image_batch.shape, label_batch.shape


print (train_generator.class_indices)

Labels = '\n'.join(sorted(train_generator.class_indices.keys()))

with open('./labels.txt', 'w') as f:
  f.write(Labels)


model=Sequential()
#Convolution blocks

model.add(Conv2D(32,(3,3), padding='same',input_shape=(300,300,3),activation='relu'))
model.add(MaxPooling2D(pool_size=2))
#model.add(SpatialDropout2D(0.5)) # No accuracy

model.add(Conv2D(64,(3,3), padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=2))
#model.add(SpatialDropout2D(0.5))

model.add(Conv2D(32,(3,3), padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=2))

#Classification layers
model.add(Flatten())

model.add(Dense(64,activation='relu'))
#model.add(SpatialDropout2D(0.5))
model.add(Dropout(0.2))
model.add(Dense(32,activation='relu'))

model.add(Dropout(0.2))
model.add(Dense(6,activation='softmax'))

filepath="./WeightsAndLabels/trained_model.h5"
checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint1]

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['acc'])

history = model.fit_generator(train_generator,
                              epochs=100,
                              steps_per_epoch=4747//32,
                              validation_data=test_generator,
                              validation_steps=527//32,
                              workers = 4,
                              callbacks=callbacks_list)