Tensorflow tf.reshape（）似乎行爲不同numpy.reshape（）

Question

我試圖訓練一個LSTM網絡，它以一種方式成功訓練，但以另一種方式拋出錯誤。在第一個例子中，我使用numpy重塑重塑了輸入數組X，並以另一種方式使用tensorflow重塑重塑它。

正常工作：

import numpy as np 
import tensorflow as tf 
import tensorflow.contrib.learn as learn 


# Parameters 
learning_rate = 0.1 
training_steps = 3000 
batch_size = 128 

# Network Parameters 
n_input = 4 
n_steps = 10 
n_hidden = 128 
n_classes = 6 

X = np.ones([1770,4]) 
y = np.ones([177]) 

# NUMPY RESHAPE OUTSIDE RNN_MODEL 
X = np.reshape(X, (-1, n_steps, n_input)) 

def rnn_model(X, y): 

    # TENSORFLOW RESHAPE INSIDE RNN_MODEL 
    #X = tf.reshape(X, [-1, n_steps, n_input]) # (batch_size, n_steps, n_input) 

    # # permute n_steps and batch_size 
    X = tf.transpose(X, [1, 0, 2]) 

    # # Reshape to prepare input to hidden activation 
    X = tf.reshape(X, [-1, n_input]) # (n_steps*batch_size, n_input) 
    # # Split data because rnn cell needs a list of inputs for the RNN inner loop 
    X = tf.split(0, n_steps, X) # n_steps * (batch_size, n_input) 

    # Define a GRU cell with tensorflow 
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden) 
    # Get lstm cell output 
    _, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32) 

    return learn.models.logistic_regression(encoding, y) 


classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes, 
             batch_size=batch_size, 
             steps=training_steps, 
             learning_rate=learning_rate) 

classifier.fit(X,y) 

不起作用：

import numpy as np 
import tensorflow as tf 
import tensorflow.contrib.learn as learn 


# Parameters 
learning_rate = 0.1 
training_steps = 3000 
batch_size = 128 

# Network Parameters 
n_input = 4 
n_steps = 10 
n_hidden = 128 
n_classes = 6 

X = np.ones([1770,4]) 
y = np.ones([177]) 

# NUMPY RESHAPE OUTSIDE RNN_MODEL 
#X = np.reshape(X, (-1, n_steps, n_input)) 

def rnn_model(X, y): 

    # TENSORFLOW RESHAPE INSIDE RNN_MODEL 
    X = tf.reshape(X, [-1, n_steps, n_input]) # (batch_size, n_steps, n_input) 

    # # permute n_steps and batch_size 
    X = tf.transpose(X, [1, 0, 2]) 

    # # Reshape to prepare input to hidden activation 
    X = tf.reshape(X, [-1, n_input]) # (n_steps*batch_size, n_input) 
    # # Split data because rnn cell needs a list of inputs for the RNN inner loop 
    X = tf.split(0, n_steps, X) # n_steps * (batch_size, n_input) 

    # Define a GRU cell with tensorflow 
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden) 
    # Get lstm cell output 
    _, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32) 

    return learn.models.logistic_regression(encoding, y) 


classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes, 
             batch_size=batch_size, 
             steps=training_steps, 
             learning_rate=learning_rate) 

classifier.fit(X,y) 

後者引發以下錯誤：

WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x7f1c67c6f750>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True. 
Traceback (most recent call last): 
    File "/home/blabla/test.py", line 47, in <module> 
    classifier.fit(X,y) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/base.py", line 160, in fit 
    monitors=monitors) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 484, in _train_model 
    monitors=monitors) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/graph_actions.py", line 328, in train 
    reraise(*excinfo) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/graph_actions.py", line 254, in train 
    feed_dict = feed_fn() if feed_fn is not None else None 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py", line 366, in _feed_dict_fn 
    out.itemset((i, self.y[sample]), 1.0) 
IndexError: index 974 is out of bounds for axis 0 with size 177 

Answer 1

一對夫婦的建議： *使用input_fn代替X，Y到fit *使用learn.Estimator代替learn.TensorFlowEstimator

由於您有小數據，下面應該可以工作。否則，您需要批量處理數據。 ``` 高清_my_inputs（）： 回報tf.constant（np.ones（[1770,4]）），tf.constant（np.ones（[177]））

Answer 2

我能得到這正與幾個小的變化：

# Parameters 
learning_rate = 0.1 
training_steps = 10 
batch_size = 8 

# Network Parameters 
n_input = 4 
n_steps = 10 
n_hidden = 128 
n_classes = 6 

X = np.ones([177, 10, 4]) # <---- Use shape [batch_size, n_steps, n_input] here. 
y = np.ones([177]) 

def rnn_model(X, y): 
    X = tf.transpose(X, [1, 0, 2]) #| 
    X = tf.unpack(X)    #| These two lines do the same thing as your code, just a bit simpler ;) 

    # Define a LSTM cell with tensorflow 
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden) 
    # Get lstm cell output 
    outputs, _ = tf.nn.rnn(lstm_cell, X, dtype=tf.float64) # <---- I think you want to use the first return value here. 

    return tf.contrib.learn.models.logistic_regression(outputs[-1], y) # <----uses just the last output for classification, as is typical with RNNs. 


classifier = tf.contrib.learn.TensorFlowEstimator(model_fn=rnn_model, 
                n_classes=n_classes, 
                batch_size=batch_size, 
                steps=training_steps, 
                learning_rate=learning_rate) 

classifier.fit(X,y) 

我覺得您遇到的主要問題是，X必須是形狀[批次，...]過去了，以適應（...）。當你使用numpy在rnn_model（）函數外重塑它時，X具有這種形狀，所以訓練起作用。

我不能說這個解決方案將產生的模型的質量，但至少它運行！