Rice_Disease_Classifier.py

from __future__ import absolute_import, print_function,division, unicode_literals

import os.path
import glob
import shutil

import tensorflow as tf
assert tf.__version__.startswith('2')

from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D,Flatten,MaxPooling2D,Dropout,Dense,Activation
from keras import regularizers
import keras

import numpy as np
import matplotlib.pyplot as plt
import pathlib

print ('successful')


gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)

print(tf.__version__)

BATCH_SIZE = 8
IMG_HEIGHT = 224
IMG_WIDTH = 224

data_dir =r"D:\qianSpace\NongYexinxichulizongheshijian\WorkSpace\archive\rice_leaf_diseases"
data_dir = pathlib.Path(data_dir)

CLASS_NAMES = np.array(['Leaf Blight','Brown Spot','Leaf Smut'])

print('Class Names: ', CLASS_NAMES)



train_path = r'D:\qianSpace\NongYexinxichulizongheshijian\WorkSpace\archive\rice_leaf_diseases'
test_path = r'D:\qianSpace\NongYexinxichulizongheshijian\WorkSpace\archive\rice_leaf_diseases'



image_train_gen = ImageDataGenerator(rescale=1./255,
                                     zoom_range=0.50,
                                     rotation_range=45,
                                     horizontal_flip=True,
                                     width_shift_range=0.15,
                                     height_shift_range=0.15)

train_data_gen = image_train_gen.flow_from_directory(train_path,
                                                     shuffle=True,
                                                     batch_size=BATCH_SIZE,
                                                     target_size=(IMG_HEIGHT,IMG_WIDTH),
                                                     class_mode='sparse')

img_val_gen = ImageDataGenerator(rescale=1./255)
val_data_gen = img_val_gen.flow_from_directory(test_path,
                                               batch_size=BATCH_SIZE,
                                               target_size=(IMG_HEIGHT,IMG_WIDTH),
                                               class_mode='sparse')





def plotImages(image_arr):
    fig,axes = plt.subplots(1, 5, figsize=(20,20))
    axes = axes.flatten()
    for img,ax in zip(image_arr,axes):
        ax.imshow(img)
    plt.tight_layout()
    plt.show()
    
    
    
# Plot a few training images
img_array = [train_data_gen[0][0][0] for i in range(5)]
plotImages(img_array)


# plot a few val images
img_array  = [val_data_gen[0][0][0] for i in range(5)]
plotImages(img_array)


# Model building
#Instatiating A convnet

model = Sequential()
model.add(Conv2D(16, (3,3), input_shape=(224,224,3), activation="relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(32, (3,3), activation="relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(64, (3,3), activation="relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(128,activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(3, activation="softmax"))

model.compile(
    optimizer = "adam",
    loss = "sparse_categorical_crossentropy",
    metrics = ['accuracy']
)

model.summary()



EPOCHS=50
history = model.fit_generator(train_data_gen, epochs=EPOCHS, validation_data=val_data_gen)


# Plot training and validation graphs
acc = history.history['accuracy']
val_accuracy = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(EPOCHS)

plt.figure(figsize=(12,12))
plt.subplot(1,2,1)
plt.plot(epochs_range,acc,label='Training Accuracy')
plt.plot(epochs_range,val_accuracy,label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1,2,2)
plt.plot(epochs_range,loss,label='Training Loss')
plt.plot(epochs_range,val_loss,label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()


#++++++++++++++++++++模型保存测试+++++++++++++++++++++++

# 模型保存，注意：仅仅是多了一个save_format的参数而已
# 注意：这里的'path_to_saved_model'不再是模型名称，仅仅是一个文件夹，模型会保存在这个文件夹之下
#model.save('path_to_saved_model', save_format='tf')
model.save('path_to_saved_model_h5.h5', save_format='h5')
# 加载模型，通过指定存放模型的文件夹来加载
#new_model = keras.models.load_model('path_to_saved_model')