python - TensorFlow 2.0 [条件 x == y 不满足元素方向 :]

Question

我正在使用 TensorFlow 2 和 Keras 训练国际象棋程序。以前，如果我在与模型训练相同的脚本中加载数据，我就可以完成这项工作，但随着数据集变得更大，腌制数据然后重新加载它会更快。当我这样做时，即使将模型修剪到一层以简化问题，我现在也会遇到这组错误。

任何人都可以阐明“条件 x == y 不持有元素明智”错误消息的含义吗？

这是代码:

import tensorflow as tf
import numpy as np
import pandas as pd
import chess
from util import *
from sklearn.model_selection import train_test_split
import pickle
import time

bb = chess.Board()

print("Reading examplesMatrix...")
examplesMatrix = pickle.load(open('examplesMatrix.sav','rb'))
examplesMatrix = examplesMatrix.T

print("Reading Y_vec...")
Y_vec = pickle.load(open('Yvector.sav','rb'))

print(examplesMatrix.shape)
print(Y_vec.shape)

X_train, X_test, y_train, y_test = train_test_split(examplesMatrix, Y_vec, test_size=0.2)

start_time = time.time()

model = tf.keras.models.Sequential([
    #tf.keras.layers.Conv1D(kernel_size=8, filters=100),
    tf.keras.layers.Dense(activation='relu', units=600)
    #tf.keras.layers.Dense(units=400, activation='relu'),
    #tf.keras.layers.Dense(units=200, activation='relu'),
    #tf.keras.layers.Dense(units=100, activation='relu'),
    #tf.keras.layers.Dense(3, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train, y_train) #, epochs=5)
model.evaluate(X_test, y_test)

print("Execution time: %s seconds ---" % (time.time() - start_time))
fname = "/home/jwales/eclipse-workspace/djwdata/science/chessmaster/keras_full_29OctC1.sav"
print("Saving as: "+fname)
model.save(fname)

这是输出: (请注意，我的棋盘是 9x8，因此我有额外的 8 个元素用于设计功能，例如棋盘总分、步数以及是谁的步法)

Reading examplesMatrix...
Reading Y_vec...
(1212827, 8, 9)
(1212827, 1)
2019-10-29 10:47:17.469971: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-10-29 10:47:17.491088: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2808000000 Hz
2019-10-29 10:47:17.491373: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4b4e870 executing computations on platform Host. Devices:
2019-10-29 10:47:17.491409: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
Train on 970261 samples
2019-10-29 10:47:18.220470: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Invalid argument: assertion failed: [] [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [32 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [32 8]
     [[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert}}]]
    32/970261 [..............................] - ETA: 3:45:10Traceback (most recent call last):
  File "kt_trainer.py", line 68, in <module>
    model.fit(X_train, y_train) #, epochs=5)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 728, in fit
    use_multiprocessing=use_multiprocessing)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 324, in fit
    total_epochs=epochs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 123, in run_one_epoch
    batch_outs = execution_function(iterator)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 86, in execution_function
    distributed_function(input_fn))
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/def_function.py", line 457, in __call__
    result = self._call(*args, **kwds)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/def_function.py", line 520, in _call
    return self._stateless_fn(*args, **kwds)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py", line 1823, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py", line 1141, in _filtered_call
    self.captured_inputs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py", line 1224, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/function.py", line 511, in call
    ctx=ctx)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute
    six.raise_from(core._status_to_exception(e.code, message), None)
  File "<string>", line 2, in raise_from
tensorflow.python.framework.errors_impl.InvalidArgumentError:  assertion failed: [] [Condition x == y did not hold element-wise:] [x (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [32 1] [y (loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [32 8]
     [[node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal/Assert/Assert (defined at /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_716]

Function call stack:
distributed_function

Answer 1

密集层需要扁平化的数据。

尝试:

   tf.keras.layers.Flatten() 

在调用致密层之前。