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This commit is contained in:
Lorenzo Iovino 2019-09-11 16:04:45 +02:00
parent ae477fc3df
commit 0109aab0f5
15 changed files with 918 additions and 586 deletions
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60
Predictor/PredictorNeuralNetwork/Predictor.py
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@ -8,15 +8,38 @@ import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import numpy as np
import os
import shutil
import random
img_width, img_height = 150, 150
train_data_dir = './UserflowPredictorSystem/predictor/datas/train'
validation_data_dir = './UserflowPredictorSystem/predictor/datas/test'
train_data_dir = './Predictor/PredictorNeuralNetwork/datas/userflows/train/'
validation_data_dir = './Predictor/PredictorNeuralNetwork/datas/userflows/test/'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 100
epochs = 20
batch_size = 16
def copytree(src, dst, symlinks=False, ignore=None):
for item in os.listdir(src):
s = os.path.join(src, item)
d = os.path.join(dst, item)
if os.path.isdir(s):
shutil.copytree(s, d, symlinks, ignore)
else:
shutil.copy2(s, d)
def populateDatas():
copytree('./Predictor/PredictorWebService/trainingImages/virtual', './Predictor/PredictorNeuralNetwork/datas/userflows/train')
offset = 0
for folder in os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train'):
numberOfFiles = len([name for name in os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train/' + folder)])
numberOfTestFiles = int(numberOfFiles / 10)
for idx in range(0,numberOfTestFiles):
file = os.listdir('./Predictor/PredictorNeuralNetwork/datas/userflows/train/' + folder)[random.randint(0,numberOfTestFiles)]
shutil.copy2("./Predictor/PredictorNeuralNetwork/datas/userflows/train/" + folder + "/" + file, './Predictor/PredictorNeuralNetwork/datas/userflows/test/' + str(idx + offset) + '.png')
shutil.copy2("./Predictor/PredictorNeuralNetwork/datas/userflows/train/" + folder + "/" + file, './Predictor/PredictorNeuralNetwork/datas/userflows/test/' + str(idx + offset) + '_SEPARATOR.png')
offset = numberOfTestFiles
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
@ -45,7 +68,7 @@ def create_model():
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
@ -66,13 +89,13 @@ def train_model(model):
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
class_mode='categorical')
model.fit_generator(
train_generator,
@ -81,7 +104,7 @@ def train_model(model):
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('./UserflowPredictorSystem/first_try2.h5')
model.save_weights('./Predictor/PredictorNeuralNetwork/weights/userflows.h5')
return model
@ -93,29 +116,26 @@ def load_trained_model(weights_path):
def predict(number, model):
img = cv2.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
im = mpimg.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
img = cv2.imread("./Predictor/PredictorNeuralNetwork/datas/userflows/test/" + str(number) + ".png")
im = mpimg.imread("./Predictor/PredictorNeuralNetwork/datas/userflows/test/" + str(number) + ".png")
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')
print(res)
model = create_model()
populateDatas()
model = train_model(model)
import os
os.getcwd()
trained_model = load_trained_model("./UserflowPredictorSystem/first_try2.h5")
trained_model = load_trained_model("./Predictor/PredictorNeuralNetwork/weights/userflows.h5")
trained_model.summary()
import random
predict(random.randint(1,12500), trained_model)
predict('lolly', model)
num = random.randint(1,10)
print(num)
predict(num, trained_model)
print(np.argmax(loaded_model.predict(img)))
print(np.argmax(trained_model.predict(img)))