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temp_prediction.py
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temp_prediction.py
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import os,shutil
import glob
import keras
import keras_applications
import random
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras import layers
from keras import models
from keras import optimizers
from keras_applications.vgg16 import VGG16
import numpy as np
from keras.preprocessing import image
from keras_preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
#
#original_dataset_dir ='C:/software/Deeplearning/test_ML/kaggle_DogsVSCats/original_data'
base_dir = './temp'
# 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,'lsc')
# os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir,'tur')
# os.mkdir(train_dogs_dir)
validation_cats_dir = os.path.join(validation_dir,'lsc')
# os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir,'tur')
# os.mkdir(validation_dogs_dir)
test_cats_dir = os.path.join(test_dir,'lsc')
# os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir,'tur')
# os.mkdir(test_dogs_dir)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])
# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better
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,
fill_mode='nearest')
train_generator = train_datagen.flow_from_directory(train_dir,
batch_size=100,
class_mode='binary',
target_size=(150, 150))
# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better
validation_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,
fill_mode='nearest')
validation_generator = validation_datagen.flow_from_directory(validation_dir,
batch_size=100,
class_mode='binary',
target_size=(150, 150))
# Expected Output:
# Found 22498 images belonging to 2 classes.
# Found 2500 images belonging to 2 classes.
# Note that this may take some time.
history = model.fit(train_generator,
epochs=15,
verbose=1,
validation_data=validation_generator)
#=================================����====================================
model.save('temperature.h5')
#=================================��ͼ=====================================
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import time
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
acc=history.history['accuracy']
val_acc=history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs=range(len(acc)) # Get number of epochs
#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.figure(1)
plt.plot(epochs, acc, 'r', "Training Accuracy")
plt.plot(epochs, val_acc, 'b', "Validation Accuracy")
plt.title('Training and validation accuracy')
plt.legend()
plt.savefig('accuracy.png')
#plt.close(1)
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.figure(2)
plt.plot(epochs, loss, 'r', "Training Loss")
plt.plot(epochs, val_loss, 'b', "Validation Loss")
plt.title('Training and validation loss')
plt.legend()
plt.savefig('loss.png')
#plt.close(2)
# Desired output. Charts with training and validation metrics. No crash :)
# predicting images
file_path = './test_imgs/'
f_names = glob.glob(file_path + '*.png')
# 把图片读取出来放到列表中
for i in range(len(f_names)):
images = image.load_img(f_names[i],target_size=(150,150))
img_tensor = image.img_to_array(images)
img_tensor = np.expand_dims(img_tensor,axis=0)
img_tensor /= 255.
print('loading no.%s image' % i)
# plt.imshow(img_tensor[0])
# plt.show()
img = np.vstack([img_tensor])
classes = model.predict(img, batch_size=10)
print(classes[0])
if classes[0] > 0.5:
print(i, " is LSC")
else:
print(i, " is a chaotic")
###################### plt show ###################
plt.show()