Question

我正在尝试使用 keras和tensorflow 编写代码，我想使用批处理大小。

您可以在https://github.com/aSafarpoor/vgg_vidclassification.git

中找到代码

它可以与以下内容一起运行：

python main.py --net resnet34s --batch_size 1 --gpu 2,3 --lr 0.001 --warmup_ratio 0.1 --optimizer adam --epochs 4-多进程1 --loss softmax --hdim 30- -wdim 30 --frame_num 80 --aggregation_mode vlad --vlad_cluster 4

当我设置批次大小== 1时，它可以工作，但是当我将其更改为另一个数字时，会出现如下错误：

Epoch 1/2
Learning rate for epoch 1 is 0.0001.

Traceback (most recent call last):

  File "main.py", line 214, in <module> main()

  File "main.py", line 145, in main verbose=1)

  File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", 
line 91, in wrapper

    return func(*args, **kwargs)

  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 

1418, in fit_generator

    initial_epoch=initial_epoch)

  File "/usr/local/lib/python2.7/dist-
packages/keras/engine/training_generator.py", line 217, in fit_generator
class_weight=class_weight)

  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 
1217, in train_on_batch
outputs = self.train_function(ins)

  File "/usr/local/lib/python2.7/dist-
packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)

  File "/usr/local/lib/python2.7/dist-
packages/keras/backend/tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)

  File "/usr/local/lib/python2.7/dist-
packages/tensorflow/python/client/session.py", line 1458, in __call__
    run_metadata_ptr)

tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible 
shapes: [2,5994] vs. [6,5994]
     [[{{node 
training/Adam/gradients/loss/prediction_loss/mul_grad/BroadcastGradientArgs}}]
]

我用不同的版本运行它，错误是相同的；像形状：[2]与[6]

您所看到的错误的重要部分是：

tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible 
    shapes: [2,5994] vs. [6,5994]
         [[{{node 
  training/Adam/gradients/loss/prediction_loss/mul_grad/BroadcastGradientArgs}}]
    ]

关于代码：

首先，

最重要的部分位于git可以访问的src文件夹中

此部分用于培训： network.fit_generator（trn_gen，

steps_per_epoch=int(len(partition['train'])//args.batch_size),
                          epochs=args.epochs,
                          max_queue_size=10,
                          callbacks=callbacks,
                          use_multiprocessing=False,
                          workers=1,
                          verbose=1)

在utils.py中阅读这样的视频：

vidcap = cv2.VideoCapture(path)
success,image = vidcap.read()
success2=success
while(success):
    image= cv2.resize(image,(dim_h,dim_w))
    sub_arr.append(image)
    success,image = vidcap.read() 
    c+=1
while(c<spec_len and success2):
    sub_arr.append(sub_arr[-1])
    c+=1

# if(len(sub_arr)>0):
big_arr=sub_arr[:spec_len]
np_arr=np.asarray(big_arr)
# print(np_arr.shape,"       GgggG")
return np_arr

然后准备好数据：

trnlist, trnlb = toolkits.get_voxceleb2_datalist(args, path='../meta/voxlb2_train.txt')
vallist, vallb = toolkits.get_voxceleb2_datalist(args, path='../meta/voxlb2_val.txt')
partition = {'train': trnlist.flatten(), 'val': vallist.flatten()}

模型函数是model.py中的resnet_3D_v1，看看：

bn_axis = 3
    if mode == 'train':
        inputs = Input(shape=input_dim, name='input')
    else:
        # inputs = Input(shape=(input_dim[0], None, input_dim[-1]), name='input')
        inputs = Input(shape=input_dim, name='input')

    # ===============================================
    #            Convolution Block 1
    # ===============================================
    x1 = Conv3D(64, (1,1,1),
                kernel_initializer='orthogonal',
                use_bias=False, trainable=True,
                kernel_regularizer=l2(weight_decay),
                padding='same',
                name='conv1_1/3x3_s1')(inputs)

    x1 = BatchNormalization(axis=bn_axis, name='conv1_1/3x3_s1/bn', trainable=True)(x1)
    x1 = Activation('relu')(x1)
    x1 = MaxPooling3D((1, 1,1), strides=(1, 1,1))(x1)

    # ===============================================
    #            Convolution Section 2
    # ===============================================
    x2 = conv_block_3D(x1, 3, [48, 48, 96], stage=2, block='a', strides=(1, 1,1), trainable=True)
    x2 = identity_block_3D(x2, 3, [48, 48, 96], stage=2, block='b', trainable=True)

    # ===============================================
    #            Convolution Section 3
    # ===============================================
    x3 = conv_block_3D(x2, 3, [96, 96, 128], stage=3, block='a', trainable=True)
    x3 = identity_block_3D(x3, 3, [96, 96, 128], stage=3, block='b', trainable=True)
    x3 = identity_block_3D(x3, 3, [96, 96, 128], stage=3, block='c', trainable=True)
    # ===============================================
    #            Convolution Section 4
    # ===============================================
    x4 = conv_block_3D(x3, 3, [128, 128, 512], stage=4, block='a', trainable=True)
    x4 = identity_block_3D(x4, 3, [128, 128, 512], stage=4, block='b', trainable=True)
    x4 = identity_block_3D(x4, 3, [128, 128, 512], stage=4, block='c', trainable=True)
    # ===============================================
    #            Convolution Section 5
    # ===============================================
    x5 = conv_block_3D(x4, 3, [512,512, 2048], stage=5, block='a', trainable=True)
    x5 = identity_block_3D(x5, 3, [512,512, 2048], stage=5, block='b', trainable=True)
    x5 = identity_block_3D(x5, 3, [512,512, 2048], stage=5, block='c', trainable=True)
    y = MaxPooling3D((1, 1,1), strides=(1, 1,1), name='mpool2')(x5)
    return inputs, y

，输入为（frame_counter，w，h，rgb = 3）

它也使用了model.py。

如何解决keras中的“不兼容形状”错误？

0 个答案: