keras训练模型时的TypeError和ValueError

Question

W0617 03:18:36.956070 139928036104064 deprecation_wrapper.py:119] From

W0617 03:18:36.965062 139928036104064 deprecation_wrapper.py:119] From

W0617 03:18:37.005445 139928036104064 deprecation_wrapper.py:119] From

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174:不推荐使用名称tf.get_default_session。请改用tf.compat.v1.get_default_session。

W0617 03:18:37.006534 139928036104064 deprecation_wrapper.py:119] From

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181:不推荐使用名称tf.configproto。请改用tf.compat.v1.ConfigProto。

W0617 03:18:39.945574 139928036104064 deprecation_wrapper.py:119] From

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834:不推荐使用名称tf.nn.fused_batch_norm。请改用tf.compat.v1.nn.fused_batch_norm。

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: UserWarning: Update your `Model` call to the Keras 2 API:

/usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790中的模型(inputs=tensor（“in...，outputs=tensor（”ac...)W0617 03:18:40.237022 139928036104064 deprecation_wrapper.py:119)：不推荐使用名称tf.train.optimizer。请改用tf.compat.v1.train.optimizer。

W0617 03:18:40.366632 139928036104064 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250:

add_dispatch_support...wrapper（来自tensorflow.python.ops.array_ops)是不推荐的，将在未来的版本中删除。更新说明：使用TF.WHERE in 2.0，它具有与NP相同的广播规则，其中_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________8)0___________________________________________________激活_________________________________________（激活）（无，28,28,9)0_______________________________________________________________________________________________________________________________________________________________________________________________________====================总参数：2,438,364个可训练参数：2,438,238个不可训练参数：-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

7 frames
TypeError: Non-valid index or slice: [1484419, 2231123, 3092786, 1496830, 493122, 736949, 1199629, 328357, 931000, 2946100, 415877,

1421951,3421223,2238167,2940723,1437219,1839514,2746665,3359532,3268348]

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tables/leaf.py in _point_selection(self, key)
    579             # handle negative indices
    580             idx = coords < 0
--> 581             coords[idx] = (coords + self.shape)[idx]
    582 
    583             # bounds check

ValueError: operands could not be broadcast together with shapes (20,) (4,)

训练数据的形状为(3450000，28，28，1)，训练标签为(3450000，345)。我以前训练过类似的模型，但从未遇到过这样的错误，如无效索引片和操作数不能一起广播。我正在使用谷歌Colab进行培训。培训代码为：

def create_model():
  inp = Input((28,28,1))
  conv1 = Conv2D(36,(2,2),padding="same",kernel_initializer="glorot_normal")(inp)
  batch1 = BatchNormalization()(conv1)
  act1 = Activation("relu")(batch1)

  conv2 = Conv2D(18,(2,2),padding="same",kernel_initializer="glorot_normal")(act1)
  batch2 = BatchNormalization()(conv2)
  act2 = Activation("relu")(batch2)

  conv3 = Conv2D(9,(2,2),padding="same",kernel_initializer="glorot_normal")(act2)
  batch3 = BatchNormalization()(conv3)
  act3 = Activation("relu")(batch3)

  out = Flatten()(act3)
  out = Dense(345)(out)
  out = Activation("softmax")(out)

  model = Model(input = inp, output = out)
  adm = optimizers.Adagrad(lr=0.02)
  model.compile(optimizer=adm,loss="categorical_crossentropy",metrics=['accuracy'])
  model.summary()

  return model

if __name__ == "__main__":
  model = create_model()
  history = model.fit(x = train_X, y = train_Y, batch_size = 20, epochs = 100, validation_data = (eval_X, eval_Y))

Answer 1

将此添加到代码的开始，指定TensorFlow版本，因为colab中的默认版本是TensorFlow V2.0

%TensorFlow_Version1.x

导入tensorflow作为tf