Question

代码如下所示并完美运行：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout

xData = np.array([[5, 3, 7], [1, 2, 6], [8, 7, 6]], dtype=np.float32)
yTrainData = np.array([[1], [0], [1]], dtype=np.float32)

model = Sequential()

model.add(Dense(64, input_dim=3, activation='relu'))

model.add(Dropout(0.2))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

model.fit(xData, yTrainData, epochs=10, batch_size=128, verbose=2)

xTestData = np.array([[2, 8, 1], [3, 1, 9]], dtype=np.float32)

resultAry = model.predict(xTestData)
print("Cal result: %s" % resultAry)

我无法在TensowFlow中编写代码，我写的内容是这样的：

import tensorflow as tf
import numpy as np

xData = np.array([[5, 3, 7], [1, 2, 6], [8, 7, 6]], dtype=np.float32)
yTrainData = np.array([[1], [0], [1]], dtype=np.float32)

x = tf.placeholder(tf.float32)
yTrain = tf.placeholder(tf.float32)

w = tf.Variable(tf.ones([64]), dtype=tf.float32)
b = tf.Variable(tf.zeros([1]), dtype=tf.float32)

y = tf.nn.relu(w * x + b)

w1 = tf.Variable(tf.ones([3]), dtype=tf.float32)
b1 = tf.Variable(0, dtype=tf.float32)

y1 = tf.reduce_mean(tf.nn.sigmoid(w1 * y + b1))

loss = tf.abs(y1 - tf.reduce_mean(yTrain))

optimizer = tf.train.AdadeltaOptimizer(0.1)

train = optimizer.minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()

sess.run(init)

for i in range(10):

    for j in range(3):
        result = sess.run([loss, y1, yTrain, x, w, b, train], feed_dict={x: xData[j], yTrain: yTrainData[j]})

        if i % 10 == 0:
            print("i: %d, j: %d, loss: %10.10f, y1: %f, yTrain: %s, x: %s" % (i, j, float(result[0]), float(result[1]), yTrainData[j], xData[j]))

result = sess.run([y1, loss], feed_dict={x: [1, 6, 0], yTrain: 0})
print(result)

但是我跑步时会出现以下错误，

Traceback (most recent call last):
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1327, in _do_call
    return fn(*args)
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1306, in _run_fn
    status, run_metadata)
  File "C:\Python36\lib\contextlib.py", line 88, in __exit__
    next(self.gen)
  File "C:\Python36\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [64] vs. [3]
         [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable/read, _arg_Placeholder_0_0)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "testidc.py", line 36, in <module>
    result = sess.run([loss, y1, yTrain, x, w, b, train], feed_dict={x: xData[j], yTrain: yTrainData[j]})
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 895, in run
    run_metadata_ptr)
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1124, in _run
    feed_dict_tensor, options, run_metadata)
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1321, in _do_run
    options, run_metadata)
  File "C:\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [64] vs. [3]
         [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable/read, _arg_Placeholder_0_0)]]

Caused by op 'mul', defined at:
  File "testidc.py", line 15, in <module>
    y = tf.nn.relu(w * x + b)
  File "C:\Python36\lib\site-packages\tensorflow\python\ops\variables.py", line 705, in _run_op
    return getattr(ops.Tensor, operator)(a._AsTensor(), *args)
  File "C:\Python36\lib\site-packages\tensorflow\python\ops\math_ops.py", line 865, in binary_op_wrapper
    return func(x, y, name=name)
  File "C:\Python36\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1088, in _mul_dispatch
    return gen_math_ops._mul(x, y, name=name)
  File "C:\Python36\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 1449, in _mul
    result = _op_def_lib.apply_op("Mul", x=x, y=y, name=name)
  File "C:\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "C:\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 2630, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "C:\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1204, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Incompatible shapes: [64] vs. [3]
         [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable/read, _arg_Placeholder_0_0)]]

主要原因是W的形状必须与TensowFlow中的x相同，但在Keras中，隐藏的Dense层可能比输入具有更多的节点（例如示例中的64）。

我需要等效TensorFlow代码的帮助而不是Keras代码。感谢。

Answer 1

这是一个使用tf.estimator.Estimator框架的示例：

import tensorflow as tf
import numpy as np

# The model
def model(features):
  dense = tf.layers.dense(inputs=features['x'], units=64, activation=tf.nn.relu)
  dropout = tf.layers.dropout(dense, 0.2)
  logits = tf.layers.dense(inputs=dropout, units=1, activation=tf.nn.sigmoid)
  return logits

# Stuff needed to use the tf.estimator.Estimator framework
def model_fn(features, labels, mode):
  logits = model(features)

  predictions = {
    'classes': tf.argmax(input=logits, axis=1),
    'probabilities': tf.nn.softmax(logits)
  }

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

  loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)

  # Configure the training op
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.RMSPropOptimizer(learning_rate=1e-4)
    train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
  else:
    train_op = None

  accuracy = tf.metrics.accuracy(
      tf.argmax(labels, axis=1), predictions['classes'])

  metrics = {'accuracy': accuracy}

  # Create a tensor named train_accuracy for logging purposes
  tf.identity(accuracy[1], name='train_accuracy')
  tf.summary.scalar('train_accuracy', accuracy[1])

  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=predictions,
      loss=loss,
      train_op=train_op,
      eval_metric_ops=metrics)

# Setting up input for the model
def input_fn(mode, batch_size):
  # function that processes your input and returns two tensors "samples" and "labels"
  # that the estimator will use to fetch input batches.
  # See https://www.tensorflow.org/get_started/input_fn for how to write this function.
  return samples, labels

# Using the model
def main():
  # Create the Estimator
  classifier = tf.estimator.Estimator(
    model_fn=model_fn, model_dir='some_dir')

  # Train the model

  # NOTE: I use this to make it compatible with your example, but you should 
  #       defnitely set up your own input_fn above
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": np.array([[5, 3, 7], [1, 2, 6], [8, 7, 6]], dtype=np.float32)},
    y=np.array([[1], [0], [1]]),
    num_epochs=10,
    batch_size=128,
    shuffle=False)

  classifier.train(
      input_fn=train_input_fn,
      steps=20000, # change as needed
      )

  # Predict on new data
  predict_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": np.array([[5, 3, 7], [1, 2, 6], [8, 7, 6]], dtype=np.float32)},
    num_epochs=1,
    batch_size=1,
    shuffle=False)

  predictions_iterator = classifier.predict(
      input_fn=predict_input_fn)
  print('Predictions results:')
  for pred in predictions_iterator:
    print(pred)

这里有很多内容，所以我会尝试逐一解释这些块。

模型

模型在单独的tf.layers函数中定义为model的组合。这样做是为了保持实际model_fn（Estimator框架所需的）独立于模型体系结构。该函数采用features参数，该参数是对input_fn的调用的输出（见下文）。在此示例中，由于我们正在使用tf.estimator.inputs.numpy_input_fn，因此功能是包含项x:input_tensor的字典。我们使用输入张量作为模型图的输入。

`model_fn`

此功能是框架所必需的，用于生成Estimator的规范，该规范取决于正在使用的估计值mode。通常情况下，用于预测的估算器将比用于培训时的操作少（您没有损失，优化器等）。此功能负责为三种可能的操作模式（预测，评估，培训）添加模型图所需的所有内容。

将其分解为逻辑部分，我们有：

预测：我们只需要模型图，预测和相应的预测标签（我们可以跳过标签，但在这里使用它很方便）。
评估：我们需要预测的所有内容：损失函数，评估的一些指标以及可选的一些摘要，以便在Tensorboard中可视化指标。
培训：我们需要评估的所有内容：来自优化工具的培训操作（在您的样本中，RMSProp）

`input_fn`

这是我们为估算工具提供输入的地方。请查看Building Input Functions with tf.estimator，了解有关自定义input_fn应如何显示的指南。例如，我们将使用框架中的numpy_input_fn函数。请注意，通常一个input_fn根据mode参数处理所有操作模式。由于我们正在使用numpy_input_fn，因此我们需要两个不同的实例来进行培训和预测，以便根据需要提供数据。

`main`

这里我们实际上训练和使用估算器。首先，我们使用我们指定的Estimator获得model_fn个实例，然后我们调用train()并等待培训结束。完成后，调用predict()会返回iterable，您可以使用该Get-VMHost -name hostname | get-view | Select-Object -ExpandProperty MoRef获取您预测的数据集中所有样本的预测结果。

Answer 2

这已经有几个月了，但值得注意的是，绝对没有理由不使用具有张量流的keras。 It's even part of the tensorflow library now!

因此，如果你想要完全控制你的张量，但仍想使用keras＆＃39;图层，您可以通过使用keras原样轻松实现这一目标：

passport.deserializeUser((id, done) => {
  User.findById(id).exec()
    .then(user => {
      if (user) {// user may be null
        done(null, user);
      } else {
        done(new Error("User not found"), null);
      }  
   })
   .catch(error => {
     done(error, false);
   });
});

更多相关信息，keras＆＃39;创作者对此提出了pretty cool post。

什么是TensorFlow中这个Keras代码的等价物？

2 个答案:

模型

`model_fn`

`input_fn`

`main`