Question

在Python 3.5.2中使用TensorFlow 1.3.0版。我正在尝试建立一个卷积神经网络，它接收69行和44个独立值的数据来执行二进制分类（即跳转或跌落），但不断收到错误：

    ValueError: Shapes (69, 44, 2) and (69, 2, 2) are incompatible

我不确定代码在何处将大小从44列更改为2.我构建的网络代码主要来自MNIST数据库的tensorflow教程，该教程已经过修改以解决我的数据/问题。主要区别在于，我使用的是conv1d（）而不是conv2d（），而且我没有使用任何池化层。代码如下：

def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  # Input Layer
  #-1 will be filled by a 69 later on
  #44 used for independent vars, 1 must be used for input in tf.layers.conv1d()
  input_layer = tf.reshape(features["x"], [-1, 44, 1])

  # Convolutional Layer #1
  conv1 = tf.layers.conv1d(
      inputs=input_layer,
      filters=32,
      kernel_size=1,
      padding="same",
      activation=tf.nn.relu,
      name = "Alan")

  # Convolutional Layer #2
  conv2 = tf.layers.conv1d(
      inputs=conv1,
      filters=64,
      kernel_size=1,
      padding="same",
      activation=tf.nn.relu,
      name = "Borris")

  # Dense Layer
  dense = tf.layers.dense(inputs=conv2, units=1024, activation=tf.nn.relu)
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

  # Logits Layer
  logits = tf.layers.dense(inputs=dropout, units=2)
  print(logits)

  predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
      # `logging_hook`.
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }

  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

  # Calculate Loss (for both TRAIN and EVAL modes)
  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=2)
  loss = tf.losses.softmax_cross_entropy(
      onehot_labels=onehot_labels, logits=logits)

  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

  # Add evaluation metrics (for EVAL mode)
  eval_metric_ops = {
      "accuracy": tf.metrics.accuracy(
          labels=labels, predictions=predictions["classes"])}
  return tf.estimator.EstimatorSpec(
      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

只要我运行eval_input_fn：

，就会出现问题

jumpupdown_classifier = tf.estimator.Estimator(
    model_fn=cnn_model_fn, model_dir="/tmp/jumpupdown_convnet_model")

tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=50)

# Train the model
train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": mTrainDat},
      y=mTrainLab,
      batch_size=69,
      num_epochs=None,
      shuffle=True)

jumpupdown_classifier.train(
      input_fn=train_input_fn,
      steps=20000,
      hooks=[logging_hook])

# Evaluate the model and print results
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": mTestDat},
      y=mTestLab,
      num_epochs=1,
      shuffle=False)
eval_results = jumpupdown_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)

为了澄清一点，mTrainDat如下所示：

array([[ 0.23971076,  0.24687247, -0.22665831, ...,  0.73542702,
        -0.57439029, -0.22714323],
       [ 0.32290912,  0.61977148, -0.4258523 , ...,  0.73542702,
         0.74438596, -0.57915074],
       [ 0.41255337,  0.2795991 , -0.56257713, ..., -0.30046052,
         0.74438596,  0.74211949],
       ..., 
       [ 0.56027043,  1.63016009,  1.36453617, ...,  0.57338732,
         0.74438596,  0.56973094],
       [-0.1470048 ,  1.23301661,  0.63612264, ...,  0.73542702,
         0.58223492,  0.74211949],
       [-0.46544313,  1.02278185,  0.30256116, ..., -1.38631892,
         0.74438596,  0.57966185]], dtype=float32)

mTrainLab看起来像这样：

array([[ 1.,  0.],
   [ 1.,  0.],
   [ 0.,  1.],
   [ 1.,  0.],
   [ 1.,  0.],
    ........
   [ 0.,  1.],
   [ 0.,  1.],
   [ 1.,  0.]], dtype=float32)

mTestDat和mTestLab看起来相似，但只包含6行。

我试图改变价值观以试图诊断问题，但一直没有成功。任何帮助表示赞赏。

ValueError：形状（69,44,2）和（69,2,2）不兼容

0 个答案: