Question

自从升级到Keras 2后，我在尝试微调ResNet50时看到了南方的损失。如果我使用单个卷积层（下面已注释掉）而不是resnet，则损失和准确性看起来不错。我错过了Keras 2改变的东西吗？

from keras.applications.resnet50 import ResNet50
from keras.layers import Flatten, Dense, Input, Conv2D, Activation, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
from keras.optimizers import SGD
import numpy as np

inp = Input(batch_shape=(32, 224, 224, 3), name='input_image')

### resnet
modelres = ResNet50(weights="imagenet", include_top=False, input_tensor=inp)
x = modelres.output
x = Flatten()(x)

### single convolutional layer
#x = Conv2D(32, (3,3))(inp)
#x = Activation('relu')(x)
#x = MaxPooling2D(pool_size=(3,3))(x)
#x = Flatten()(x)
#x = Dense(units=32)(x)
predictions = Dense(units=2, kernel_initializer="he_normal", activation="softmax")(x) 

model = Model(inputs=inp, outputs=predictions)
model.compile(SGD(lr=.001, momentum=0.9), "categorical_crossentropy", metrics=["accuracy"])

# generate images of all ones with the same label
def gen():
    while True:
        x_data = np.ones((32,224,224,3)).astype('float32')
        y_data = np.zeros((32,2)).astype('float32')
        y_data[:,1]=1.0
        yield x_data, y_data

model.fit_generator(gen(), 10, validation_data=gen(), validation_steps=1)

model.summary()的开头和结尾如下：

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to
====================================================================================================
input_image (InputLayer)         (32, 224, 224, 3)     0
____________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D) (32, 230, 230, 3)     0
____________________________________________________________________________________________________
conv1 (Conv2D)                   (32, 112, 112, 64)    9472

...

avg_pool (AveragePooling2D)      (32, 1, 1, 2048)      0
____________________________________________________________________________________________________
flatten_1 (Flatten)              (32, 2048)            0
____________________________________________________________________________________________________
dense_1 (Dense)                  (32, 2)               4098
====================================================================================================

培训输出是：

Epoch 1/1
10/10 [==============================] - 30s - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00

Answer 1

当我将后端切换到tensorflow而不是theano时，一切正常。在keras 2中看起来有关theano实现的一些内容。

使用Keras 2的ResNet50纳米损耗

1 个答案: