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我正在 Google Colab 中尝试一些代码。使用 CPU 它工作正常，但是当我切换到 GPU 时它显示错误。

自包含代码：

import numpy as np
import tensorflow as tf
import keras
from keras.layers import Input, BatchNormalization, Activation
from keras.layers import ZeroPadding2D, MaxPooling2D, Dense
from keras.layers import Reshape, Add, Dropout
from keras.layers import Conv2D
from keras.layers import Conv3DTranspose, Conv2DTranspose
from keras.initializers import VarianceScaling
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD
import sys

# hyperparameters
BATCH_NORM_MOMENTUM = 0.1
BATCH_NORM_EPS = 1e-5
KERNEL_REGULARIZER = 0.0001
batchSize = 4

sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)


def step1(input_shape = (3, 256, 256)):

    step = 'step1_'
    X_input = Input(input_shape, name = step + 'input')

    X = Conv2D(64, (7, 7), strides = (2, 2),  padding='same', data_format = 'channels_first', kernel_initializer="he_normal",kernel_regularizer=l2(KERNEL_REGULARIZER), name = step+'b1_conv_a',)(X_input)
    X = BatchNormalization(axis = 1, momentum=BATCH_NORM_MOMENTUM, epsilon = BATCH_NORM_EPS, name = step+'b1_bn_a')(X)
    X = Activation('relu', name = step+'b1_act_a')(X)
    X = MaxPooling2D((3, 3), strides=(2, 2), data_format='channels_first', padding='same', name = step + 'b1_maxpool2d_a')(X)
    print(X.shape)
    model = Model(inputs = X_input, outputs = X, name='step1')

    return model

step1Model = step1((3,256,256))

错误：

ValueError: Shape must be rank 1 but is rank 0 for 'step1_b1_bn_a/cond/Reshape_4' (op: 'Reshape') with input shapes: [1,64,1,1], [].

为什么使用 CPU 和 GPU 之间存在这种差异？

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1 回答 1

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这可能分别与CPUtensorflow和tensorflow-gpuGPU 内核上的软件包有关。

您可以绕过它，但从中删除axis = 1BatchNormalization layer

改变：

X = BatchNormalization(axis = 1, momentum=BATCH_NORM_MOMENTUM, epsilon = BATCH_NORM_EPS, name = step+'b1_bn_a')(X)

到：

X = BatchNormalization(momentum=BATCH_NORM_MOMENTUM, epsilon = BATCH_NORM_EPS, name = step+'b1_bn_a')(X)
于 2019-06-28T13:11:57.767 回答