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Question

Keras/Tensorflow多GPU InvalidArgumentError我想尝试用后端Tensorflow在Keras多GPU训练。

我想在这里的功能描述make_parallel：https://medium.com/@kuza55/transparent-multi-gpu-training-on-tensorflow-with-keras-8b0016fd9012。造成这种情况的代码是在这里（更新Keras 2）：

from keras.layers import concatenate 
from keras.layers.core import Lambda 
from keras.models import Model 

import tensorflow as tf 

def make_parallel(model, gpu_count): 
    def get_slice(data, idx, parts): 
     shape = tf.shape(data) 
     size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0) 
     stride = tf.concat([ shape[:1] // parts, shape[1:]*0 ],axis=0) 
     start = stride * idx 
     return tf.slice(data, start, size) 

    outputs_all = [] 
    for i in range(len(model.outputs)): 
     outputs_all.append([]) 

    #Place a copy of the model on each GPU, each getting a slice of the batch 
    for i in range(gpu_count): 
     with tf.device('/gpu:%d' % i): 
      with tf.name_scope('tower_%d' % i) as scope: 

       inputs = [] 
       #Slice each input into a piece for processing on this GPU 
       for x in model.inputs: 
        input_shape = tuple(x.get_shape().as_list())[1:] 
        slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x) 
        inputs.append(slice_n)     

       outputs = model(inputs) 

       if not isinstance(outputs, list): 
        outputs = [outputs] 

       #Save all the outputs for merging back together later 
       for l in range(len(outputs)): 
        outputs_all[l].append(outputs[l]) 

    # merge outputs on CPU 
    with tf.device('/cpu:0'): 
     merged = [] 
     for outputs in outputs_all: 
      merged.append(concatenate(outputs, axis=0)) 

     return Model(inputs=model.inputs, outputs=merged) 

我创建了一个模型：

model = make_parallel(create_model(...), 4) 
model.compile(optimizer='adam', loss='mse', metrics=['mae', 'mse',]) 

运行适合它培养了一个时代，然后用下面的异常崩溃后：

InvalidArgumentError (see above for traceback): Incompatible shapes: [120,1] vs. [122,1] 
    [[Node: training_6/Adam/gradients/loss_10/concatenate_7_loss/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@loss_10/concatenate_7_loss/sub"], _device="/job:localhost/replica:0/task:0/gpu:0"](training_6/Adam/gradients/loss_10/concatenate_7_loss/sub_grad/Shape/_10935, training_6/Adam/gradients/loss_10/concatenate_7_loss/sub_grad/Shape_1)]] 
    [[Node: training_6/Adam/gradients/concatenate_7/concat_grad/Slice_1/_11003 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:1", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_4728_training_6/Adam/gradients/concatenate_7/concat_grad/Slice_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:1"]()]] 

不顺心的事时，得到对不同的GPU模型的梯度相结合的阶段。在异常的不相容形状尺寸都与以某种方式批量大小（128此处）（即改变批量大小改变不相容形状尺寸）。

Answer 1

您的问题似乎是一个类似于报道here。看起来输入数据大小必须是GPU数量的倍数。

从链接：

样本的数量只是需要GPU的总数的多发。

Ex。我的输入中有68531个样本，一旦我用8个GPU将其削减到68528，它就可以正常工作。