CIFAR10の例：KerasからTensorflowへ

Question

私はKerasのCIFAR10の実例を持っています。私はTFに変換しようとしています。私はPythonとTFにかなり新しいです。私はhereから多くの資料を取り入れましたが、私はデータセットをロードして準備するKeras関数を保持していました。これにより、データセットも同じになります。

問題は、バッチを準備する方法に問題がある可能性があります。あなたがTFバージョンで見るコメント付きコードは、それほど遅くなかったことを除いて、何の違いもありませんでした。コードのその部分は、batch_sizeの画像とラベルをデータセットからepoch_xとepoch_yにコピーすることになっています。

とにかく、損失値が時間とともに減少しても、TFバージョンの精度は0.1（ランダム出力）に止まるという問題があります。前のexperienceから、これはデータセットの問題が原因であることがあります。

以下の両方の例のコード。 TFバージョンの問題が考えられる場合は、教えてください。事前にどうもありがとうございました。

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：私はこの信じられないほどの不一致の原因を発見した

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})) 

Answer 1

悲惨な汗を流しての日後：体重の初期化！明らかに、Kerasはデフォルトで0と0.05の間の一様分布を使用しています。同じようにTFコードを変更した後は、うまくいっています。うまく立ち上がり、もはや0.1で立ち往生しなくなりました。