我在Keras有一个CIFAR10的工作示例，我正在尝试将其转换为TF。我是Python和TF的新手。我从here改编了很多材料，但我保留了加载和准备数据集的Keras函数。这也可以确保数据集相同。
问题可能在于我准备批次的方式。您在TF版本中看到的注释代码没有任何区别，只是它的速度慢了很多。该部分代码应该将 batch_size 图像和标签从数据集复制到epoch_x和epoch_y。
无论如何，问题在于TF版本精度被固定在0.1（随机输出），即使损失值随时间减小。从以前的experience开始，这有时是由于数据集问题造成的。
以下两个示例的代码。如果你能想到TF版的任何问题，请告诉我。非常感谢你提前。
Keras：
from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
import numpy as np
#seed = 7
#np.random.seed(seed)
batch_size = 50
nb_classes = 10
nb_epoch = 200
data_augmentation = False
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
#sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
sgd= Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(X_train)
# fit the model on the batches generated by datagen.flow()
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test))
model.save('model3.h5')
Tensorflow：
from __future__ import print_function
from keras.datasets import cifar10
from keras.utils import np_utils
import tensorflow as tf
import numpy as np
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
batch_size = 50
nb_classes = 10
nb_epoch = 200
#seed = 7
#np.random.seed(seed)
epoch_x=np.zeros((batch_size,img_rows, img_cols,img_channels)).astype('float32')
print('epoch_x shape:', epoch_x.shape)
epoch_y=np.zeros((batch_size,nb_classes)).astype('float32')
print('epoch_y shape:', epoch_y.shape)
num_train_examples=50000
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print('X_train shape:', X_train.shape[1:])
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
print('Y_train shape:', Y_train.shape)
Y_test = np_utils.to_categorical(y_test, nb_classes)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
# size of window movement of window
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
# Define network
# TF graph
img = tf.placeholder(tf.float32, shape=(None,img_rows, img_cols,img_channels))
labels = tf.placeholder(tf.float32, shape=(None, nb_classes))
weights = {'W_conv0':tf.Variable(tf.random_normal([3,3,3,32])),
'W_conv1':tf.Variable(tf.random_normal([3,3,32,32])),
'W_conv2':tf.Variable(tf.random_normal([3,3,32,64])),
'W_conv3':tf.Variable(tf.random_normal([3,3,64,64])),
'W_fc':tf.Variable(tf.random_normal([8*8*64,512])),
'out':tf.Variable(tf.random_normal([512, nb_classes]))}
biases = {'b_conv0':tf.Variable(tf.random_normal([32])),
'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_conv3':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([512])),
'out':tf.Variable(tf.random_normal([nb_classes]))}
conv0 = conv2d(img, weights['W_conv0']) + biases['b_conv0']
conv0 = tf.nn.relu(conv0)
conv1 = conv2d(conv0, weights['W_conv1']) + biases['b_conv1']
conv1 = tf.nn.relu(conv1)
conv1 = maxpool2d(conv1)
conv1 = tf.nn.dropout(conv1,0.25)
conv2 = conv2d(conv1, weights['W_conv2']) + biases['b_conv2']
conv2 = tf.nn.relu(conv2)
conv3 = conv2d(conv2, weights['W_conv3']) + biases['b_conv3']
conv3 = tf.nn.relu(conv3)
conv3 = maxpool2d(conv3)
conv3 = tf.nn.dropout(conv3,0.25)
fc = tf.reshape(conv3,[-1, 8*8*64])
fc = tf.matmul(fc, weights['W_fc'])+biases['b_fc']
fc = tf.nn.relu(fc)
fc = tf.nn.dropout(fc,0.5)
prediction = tf.matmul(fc, weights['out'])+biases['out']
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,labels) )
optimizer = tf.train.AdamOptimizer().minimize(cost)
#optimizer = tf.train.AdamOptimizer(learning_rate=1e-3,epsilon=0.1).minimize(cost)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.0001).minimize(cost)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(nb_epoch):
epoch_loss = 0
for i in range(int(num_train_examples/batch_size)):
# batch = mnist_data.train.next_batch(batch_size)
for j in range(batch_size):
epoch_x[j]=X_train[i*batch_size+j]
epoch_y[j]=Y_train[i*batch_size+j]
## for j in range(batch_size):
## for row in range(img_rows):
## for col in range(img_cols):
## for ch in range(img_channels):
## epoch_x[j][row][col][ch]=X_train[i*batch_size+j][row][col][ch]
## for j in range(batch_size):
## for t in range(nb_classes):
## epoch_y[j][t]=Y_train[i*batch_size+j][t]
_, c = sess.run([optimizer, cost],feed_dict={img: epoch_x,labels: epoch_y})
epoch_loss += c
print('Epoch', epoch, 'completed out of',nb_epoch,'loss:',epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float32'))
print('Accuracy:',accuracy.eval({img: X_test,labels: Y_test}))
答案 0 :(得分：3)
经过数天的悲惨劳累，我发现了这种令人难以置信的差异的原因：体重初始化！显然，默认情况下，Keras使用0到0.05之间的均匀分布。更改TF代码后也做了同样的事情，它变得更好了，很好地提升，不再停留在0.1。