Question

我正在学习深度学习课程，我现在正在做的是CNN网络，火车设置为8000张照片4000只猫和4000条狗，训练设置为2000/2000，我用于图像的大小是64x64（带RGB）。我正在使用带有32个滤波器的2个conv2d / maxpool层的Keras，一个平坦层和两个128和1个输出的密集层。我的问题是，此设置在每个纪元执行15分钟，并执行25个纪元，这意味着至少需要进行6个小时的训练，有时在某些纪元上的培训有时会冻结，有时为7999/8000，而我在Windows 10和蟒蛇蟒蛇3.7上运行此设置和TensorFlow 1.13。这是好的表现还是我可以改善呢？我期望新的Turing架构具有更好的性能。

# -*- coding: utf-8 -*-

# Part 1 - Building the convolutional neural network
import tensorflow as tf
from keras import backend as K

config = tf.ConfigProto(intra_op_parallelism_threads=6,
                        inter_op_parallelism_threads=6, 
                        allow_soft_placement=True,
                        device_count = {'CPU' : 1,
                                        'GPU' : 1}
                       )

session = tf.Session(config=config)
K.set_session(session)
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

weights = classifier.get_weights()

#Part 2 - Fiting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

classifier.save("my first model")

谢谢

Tensorflow在RTX 2060上运行缓慢

0 个答案: