119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
from keras.models import Sequential
|
|
from keras.layers import Activation, Dropout, Flatten, Dense
|
|
from keras import backend as K
|
|
from keras.preprocessing.sequence import pad_sequences
|
|
from keras.preprocessing.image import ImageDataGenerator
|
|
from keras.layers import Conv2D, MaxPooling2D
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.image as mpimg
|
|
import cv2
|
|
import numpy as np
|
|
|
|
img_width, img_height = 150, 150
|
|
train_data_dir = './Predictor/PredictorNeuralNetwork/datas/catVsDog/train/'
|
|
validation_data_dir = './Predictor/PredictorNeuralNetwork/datas/catVsDog/test/'
|
|
nb_train_samples = 2000
|
|
nb_validation_samples = 800
|
|
epochs = 100
|
|
batch_size = 16
|
|
|
|
|
|
if K.image_data_format() == 'channels_first':
|
|
input_shape = (3, img_width, img_height)
|
|
else:
|
|
input_shape = (img_width, img_height, 3)
|
|
|
|
def create_model():
|
|
model = Sequential()
|
|
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
|
|
model.add(Conv2D(32, (3, 3)))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
|
|
model.add(Conv2D(64, (3, 3)))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
|
|
model.add(Flatten())
|
|
model.add(Dense(64))
|
|
model.add(Activation('relu'))
|
|
model.add(Dropout(0.5))
|
|
model.add(Dense(1))
|
|
model.add(Activation('sigmoid'))
|
|
|
|
|
|
model.compile(loss='binary_crossentropy',
|
|
optimizer='rmsprop',
|
|
metrics=['accuracy'])
|
|
return model
|
|
|
|
def train_model(model):
|
|
# this is the augmentation configuration we will use for training
|
|
train_datagen = ImageDataGenerator(
|
|
rescale=1. / 255,
|
|
shear_range=0.2,
|
|
zoom_range=0.2,
|
|
horizontal_flip=True)
|
|
|
|
# this is the augmentation configuration we will use for testing:
|
|
# only rescaling
|
|
test_datagen = ImageDataGenerator(rescale=1. / 255)
|
|
|
|
train_generator = train_datagen.flow_from_directory(
|
|
train_data_dir,
|
|
target_size=(img_width, img_height),
|
|
batch_size=batch_size,
|
|
class_mode='binary')
|
|
|
|
validation_generator = test_datagen.flow_from_directory(
|
|
validation_data_dir,
|
|
target_size=(img_width, img_height),
|
|
batch_size=batch_size,
|
|
class_mode='binary')
|
|
|
|
model.fit_generator(
|
|
train_generator,
|
|
steps_per_epoch=nb_train_samples // batch_size,
|
|
epochs=epochs,
|
|
validation_data=validation_generator,
|
|
validation_steps=nb_validation_samples // batch_size)
|
|
|
|
model.save_weights('./Predictor/PredictorNeuralNetwork/weights/catVsDog2.h5')
|
|
return model
|
|
|
|
|
|
|
|
def load_trained_model(weights_path):
|
|
model = create_model()
|
|
model.load_weights(weights_path)
|
|
return model
|
|
|
|
|
|
def predict(number, model):
|
|
img = cv2.imread("./Predictor/PredictorNeuralNetwork/datas/catVsDog/test/" + str(number) + ".jpg")
|
|
im = mpimg.imread("./Predictor/PredictorNeuralNetwork/datas/catVsDog/test/" + str(number) + ".jpg")
|
|
plt.imshow(im)
|
|
img = cv2.resize(img, (img_width,img_height))
|
|
img = img.reshape(1, img_width, img_height, 3)
|
|
res = model.predict(img)
|
|
if res == 1:
|
|
print('DOG')
|
|
else:
|
|
print('CAT')
|
|
|
|
model = create_model()
|
|
model = train_model(model)
|
|
|
|
import os
|
|
os.getcwd()
|
|
|
|
trained_model = load_trained_model("./Predictor/PredictorNeuralNetwork/weights/catVsDog.h5")
|
|
trained_model.summary()
|
|
import random
|
|
predict(random.randint(1,12500), trained_model)
|
|
|
|
|
|
print(np.argmax(trained_model.predict(img)))
|