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new train.py
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new train.py
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import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
# from PIL import Image
import cv2
import matplotlib.pyplot as plt
import sys
from tqdm import tqdm
path = os.getcwd()
clean_path = os.path.join(path,r"Clean_hsv")
unclean_path = os.path.join(path,r"Unclean_hsv")
CLASS_NAMES = np.array(['Clean','Unclean'])
image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255,validation_split=0.2,horizontal_flip=True,vertical_flip=True)
EPOCHS = 10
BATCH_SIZE = 32
IMG_HEIGHT = 224
IMG_WIDTH = 224
# train_data_gen = image_generator.flow_from_directory(directory=str(path), \
# batch_size=BATCH_SIZE, \
# shuffle=True, \
# target_size=(IMG_HEIGHT, IMG_WIDTH), \
# classes = list(CLASS_NAMES),\
# subset='training',\
# class_mode='binary')
train_data_gen = image_generator.flow_from_directory(directory=str(path), \
batch_size=BATCH_SIZE, \
shuffle=True, \
target_size=(IMG_HEIGHT, IMG_WIDTH), \
classes = list(CLASS_NAMES),\
subset='training',\
class_mode='categorical')
from collections import Counter
counter = Counter(train_data_gen.classes)
max_val = float(max(counter.values()))
class_weights = {class_id : max_val/num_images for class_id, num_images in counter.items()}
print(class_weights)
# sys.exit()
STEPS_PER_EPOCH = train_data_gen.samples//BATCH_SIZE
# os.mkdir(os.path.join(path,r"Train Dataset"))
# cnt = 0
# name = os.path.join(path,r"Train Dataset")
# pbar = tqdm(total=36604)
# for x,y in train_data_gen:
# # print(y[0])
# for img,label in zip(x,y):
# img=np.squeeze(img)
# img*=255
# img=np.array(img,dtype=np.uint8)
# print(label)
# # n = f"{cnt}.{label}.jpg"
# # nme=os.path.join(name,n)
# # cv2.imwrite(nme,cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
# # if not cv2.imwrite(nme,cv2.cvtColor(img, cv2.COLOR_RGB2BGR)):
# # print(label,img,nme)
# # raise Exception("Could not write image")
# # pbar.update()
# # cnt+=1
# plt.imshow(img)
# plt.show()
# break
# break
# # pbar.close()
# sys.exit()
# validation_generator = image_generator.flow_from_directory(
# directory=str(path), # same directory as training data
# target_size=(IMG_HEIGHT, IMG_WIDTH), \
# batch_size=BATCH_SIZE, \
# class_mode='binary', \
# classes = list(CLASS_NAMES),\
# subset='validation') # set as validation dat
validation_generator = image_generator.flow_from_directory(
directory=str(path), # same directory as training data
target_size=(IMG_HEIGHT, IMG_WIDTH), \
batch_size=BATCH_SIZE, \
class_mode='categorical', \
classes = list(CLASS_NAMES),\
subset='validation')
# os.mkdir(os.path.join(path,r"Val Dataset"))
# cnt = 0
# name = os.path.join(path,r"Val Dataset")
# pbar = tqdm(total=36604)
# for x,y in validation_generator:
# # print(y[0])
# for img,label in zip(x,y):
# # img=np.squeeze(img)
# img*=255
# img=np.array(img,dtype=np.uint8)
# # print(y[0])
# n = f"{cnt}.{label}.jpg"
# nme=os.path.join(name,n)
# cv2.imwrite(nme,cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
# if not cv2.imwrite(nme,cv2.cvtColor(img, cv2.COLOR_RGB2BGR)):
# print(label,img,nme)
# raise Exception("Could not write image")
# pbar.update()
# cnt+=1
# # plt.imshow(img)
# # plt.show()
# # break
# pbar.close()
# sys.exit()
# for x in validation_generator:
# print(x[0][1])
# plt.imshow(x[0][1])
# plt.show()
# break
# sys.exit()
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
# pre_trained_model = InceptionV3(input_shape = (IMG_HEIGHT, IMG_WIDTH, 3), \
# include_top = False, \
# weights = 'imagenet')
# pre_trained_model.summary()
# for layer in pre_trained_model.layers:
# layer.trainable = False
# last_layer = pre_trained_model.get_layer('mixed10')
# last_output = last_layer.output
# x = pre_trained_model.output
# x = layers.Conv2D(filters=128,kernel_size=5,activation='relu')(x)
# x = layers.Conv2D(filters=64,kernel_size=5,activation='relu')(x)
# x = layers.MaxPooling2D(pool_size=(2,2))
# x = layers.Dropout(0.25)(x)
# x = layers.Conv2D(filters=64,kernel_size=3,activation='relu')(x)
# x = layers.Conv2D(filters=32,kernel_size=3,activation='relu')(x)
# x = layers.MaxPooling2D(pool_size=(2,2))
# x = layers.Dropout(0.25)(x)
# x = layers.Flatten()
# x = layers.Dense(1024, activation='relu')(x)
# x = layers.Dense(512, activation='relu')(x)
# x = layers.Dense(256, activation='relu')(x)
# x = layers.Dense(128, activation='relu')(x)
# x = layers.Dropout(0.25)(x)
# predictions = layers.Dense(1, activation='sigmoid')(x)
# predictions = layers.Dense(2, activation='softmax')(x)
from tensorflow.keras import layers,Model
############################### Model we need to play with ###############
model = tf.keras.models.Sequential()
model.add(layers.Input(shape=(IMG_HEIGHT,IMG_WIDTH,3)))
model.add(layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu',))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=64,kernel_size=(3,3),activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=64,kernel_size=(3,3),activation='relu',))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Conv2D(filters=128,kernel_size=(3,3),activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(rate=0.2))
model.add(layers.Flatten())
model.add(layers.Dropout(rate=0.2))
model.add(layers.Dense(128,activation='relu'))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(2,activation='softmax'))
# model = Model(inputs=pre_trained_model.input, outputs=predictions)
#############################################################################################
# from tensorflow.keras.optimizers import RMSprop
# model = keras.Sequential()
# # model.add(layers.Input())
# model.add(layers.Conv2D(32,(3,3),activation='relu'))
# model.add(layers.MaxPool2D())
# model.add(layers.Conv2D(32,(3,3),activation='relu'))
# model.add(layers.Conv2D(32,(3,3),activation='relu'))
# model.add(layers.MaxPool2D())
# model.add(layers.Conv2D(32,(3,3),activation='relu'))
# model.add(layers.Flatten())
# model.add(layers.Dense(units=512,activation='relu'))
# model.add(layers.Dense(units=256,activation='relu'))
# model.add(layers.Dense(units=128,activation='relu'))
# model.add(layers.Dense(units=64,activation='relu'))
# model.add(layers.Dense(units=32,activation='relu'))
# model.add(layers.Dropout(rate=0.2))
# model.add(layers.Dense(units=1,activation='sigmoid'))
model.compile(optimizer='adam', loss = 'categorical_crossentropy', \
metrics = ['accuracy'])
# model.compile(optimizer='adam', loss = 'categorical_crossentropy', \
# metrics = ['accuracy'])
# categorical_crossentropy
# model.compile(optimizer=RMSprop(lr=0.001), loss = 'categorical_crossentropy', \
# metrics = ['accuracy'])
history = model.fit(
train_data_gen,\
steps_per_epoch=train_data_gen.samples//BATCH_SIZE,\
epochs=EPOCHS,\
validation_data = validation_generator, \
validation_steps = validation_generator.samples // BATCH_SIZE,\
class_weight=class_weights)
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
# acc = history.history['accuracy']
# acc = np.resize(acc,(100,1))
# plt.plot(EPOCHS,acc)
# plt.show()
# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()