np.ndarray型のデータをfor文に入れて，ニュラールネットを作成しているのですが，うまくいきません．どなたか，ご教示の程，よろしくお願い致します．以下にエラー文と該当箇所を記載いたします．

エラー文

```

    2020-11-11 14:18:42.758237: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] 　　　　　 
    Successfully opened dynamic library cudart64_100.dll
    Traceback (most recent call last):
    File "C:/Users/namae/Desktop/myo-python-1.0.4/bindsnet-master/bindsnet/preRSNN.py", line 95, in 
    <module>
    network.run(inputs=inputs, time=time)
    File "C:\Python36\lib\site-packages\bindsnet\network\network.py", line 312, in run
    if len(inputs[key].size()) == 1:
    TypeError: 'int' object is not callable
    
```

次にコード記載いたします．  

```

    import torch                                                                                      
    from bindsnet.network import Network                                                              
    from bindsnet.network.nodes import Input, LIFNodes                                                
    from bindsnet.network.topology import Connection                                                  
    from bindsnet.network.monitors import Monitor                                                     
    from include.data import get_data_set                                                           
    import numpy as np                                                                              
    import tensorflow as                                                                                              
    train_x, train_y = get_data_set()                                                              
    _BATCH_SIZE = 300                                                                          
    _CLASS_SIZE = 6time = 100                                                                      
    network = Network(dt=1.0)
    inpt = Input(n=100, sum_input=True)
    middle = LIFNodes(n=100, trace=True)
    center = LIFNodes(n=100, trace=True)
    final = LIFNodes(n=100, trace=True)
    out = LIFNodes(n=100, sum_input=True)
    inpt_middle = Connection(source=inpt, target=middle, wmin=0, wmax=1e-1)
    middle_center = Connection(source=middle, target=center, wmin=0, wmax=1e-1)
    center_final = Connection(source=center, target=final, wmin=0, wmax=1e-1)
    final_out = Connection(source=final, target=out, wmin=0, wmax=1e-1)network.add_layer(inpt, name='A')
    network.add_layer(middle, name='B')
    network.add_layer(center, name='C')
    network.add_layer(final,  name='D')
    network.add_layer(out, name='E')
    foward_connection = Connection(source=inpt, target=middle, w=0.05 + 0.1*torch.randn(inpt.n, 
    middle.n))
    network.add_connection(connection=foward_connection, source="A", target="B")
    foward_connection = Connection(source=middle, target=center, w=0.05 + 0.1*torch.randn(middle.n, 
    center.n))
    network.add_connection(connection=foward_connection, source="B", target="C")
    foward_connection = Connection(source=center, target=final, w=0.05 + 0.1*torch.randn(center.n, 
    final.n))
    network.add_connection(connection=foward_connection, source="C", target="D")
    foward_connection = Connection(source=final, target=out, w=0.05 + 0.1*torch.randn(final.n, out.n))
    network.add_connection(connection=foward_connection, source="D", target="E")
    recurrent_connection = Connection(source=out, target=out, w=0.025*(torch.eye(out.n)-1),)
    network.add_connection(connection=recurrent_connection, source="E", target="E")
    inpt_monitor = Monitor(obj=inpt, state_vars=("s","v"), time=500,)
    middle_monitor = Monitor(obj=inpt, state_vars=("s","v"), time=500,)
    center_monitor = Monitor(obj=inpt, state_vars=("s","v"), time=500,)
    final_monitor = Monitor(obj=inpt, state_vars=("s","v"), time=500,)
    out_monitor = Monitor(obj=inpt, state_vars=("s","v"), time=500,)
    or l in network.layers:
    m = Monitor(network.layers[l], state_vars=['s'], time=time)
    network.add_monitor(m, name=l)
    # network.to('cuda')
    # トレーニングデータをロードする
    # data_set = np.load("C:/Users/namae/Desktop/myo-python-1.0.4/myo-armband-nn- 
    master/data/train_set.npz")
    npzfile = np.load("C:/Users/namae/Desktop/myo-python-1.0.4/myo-armband-nn-master/data/train_set.npz")
    x = npzfile['x']
    y = npzfile['y']
    loader = zip(x, y)
    def model(_NUM_CLASSES = 4):
    x = tf.placeholder(tf.float32, shape=[None, 64], name='Input')
    y = tf.placeholder(tf.float32, shape=[None, _NUM_CLASSES], name='Output')
       return x, y                                                                                       
    grads = {}
    lr, lr_decay = 1e-2, 0.95
    criterion = torch.nn.CrossEntropyLoss()
    for i in enumerate(zip(x,y)):
        inputs = {'A': x.repeat(time,1), 'E_b': torch.ones(time, 1)}
        network.run(inputs=inputs, time=time)☚ここです
        y = torch.tensor(y).long()
        spikes = {l: network.monitor[l].get('s') for l in network.layers}
        summed_inputs = {l: network.layers[l].summed for l in network.layers}
        output = spikes['E'].sum(-1).softmax(0)
        predicted = output.argmax(1).item()
    # 損失とSGDの更新
    grads['dl/df'] = summed_inputs['E'].softmax(0)
    grads['dl/df'][y] -= 1
    grads['dl/dw'] = torch.ger(summed_inputs['A'], grads['dl/df'])
    network.connections['A','E'].w -= lr*grads['dl/dw']
    
    # 減衰率
    if i > 0 and i % 500 == 0:
        lr = lr_decay
    network.reset()
    
```
CC BY-SA 4.0
2020-11-11T05:31:36.400
71860
2021-01-01T08:01:05.093
41671
question
python numpy pytorch
'int' object is not callableを直したいです．
> if len(inputs[key].size()) == 1:  
>  TypeError: 'int' object is not callable

intのオブジェクトは呼び出しできない、と言うエラーなので、  
その行を見直してみればいいかと。

inputs[key].sizeに何が入ってるんでしょう
CC BY-SA 4.0
2020-11-11T06:27:09.507
71863
2020-11-11T06:27:09.507
27481
71860
answer