元組索引超出範圍與LSTM神經網絡。 Python，帶有Tensorflow的Keras

Question

目前正在研究使用帶有Tensorflow後端的Keras的問題。

我想創建與下面的代碼（例如最小，不包括我的真實數據）神經網絡，但它給出了錯誤：

「的元組索引超出範圍」的大小input_shape以下行：

model.add(TimeDistributed(LSTM(32,return_sequences=True),input_shape=trainData.shape[1:])) 

的錯誤似乎是recurrent.py文件中的行964：

self.input_dim = input_shape[2] 

當它試圖訪問input_shape [2]。我通過形狀只有兩個數字（數據的時間序列長度和通道數量）。我通過的形狀是（100000,2）。

我想這條線試圖訪問我沒有傳遞給它的東西的索引。

所以我的問題是我應該如何使用我的神經網絡配置的輸入形狀？

我使用的是Keras版本2.0.3和Tensorflow版本1.0.1。

編輯：recurrent.py是與Keras（我認爲）提供的文件。我不想開始編輯它，以防萬一我真的摔了一跤。

# import the necessary packages 
from sklearn.cross_validation import train_test_split 
from keras.models import Sequential 
from keras.layers import Activation 
from keras.optimizers import SGD 
from keras.layers import LSTM 
from keras.layers.wrappers import TimeDistributed 
from keras.utils import np_utils 
import numpy as np 
numClasses = 10 
time_points = 100000 
num_chans = 2 

iq = np.empty((time_points,0,1), int)# creates empty numpy array. 
labels = np.empty([0,1]) 

raw_data = np.random.rand(500,time_points,num_chans) 
labels = np.random.randint(numClasses, size=(500, 1)) 
one_hot_labels = np_utils.to_categorical(labels, num_classes=None) 

print(one_hot_labels.shape) 

# partition the data into training and testing splits, using 75% 
# of the data for training and the remaining 25% for testing 
print("[INFO] constructing training/testing split...") 
(trainData, testData, trainLabels, testLabels) = train_test_split(
raw_data, one_hot_labels, test_size=0.25, random_state=42) 

trainLabels = trainLabels.reshape(375,10,1) 
testLabels = testLabels.reshape(125,10,1) 

print(trainData.shape) 
print(testData.shape) 
print(trainLabels.shape) 
print(testLabels.shape) 
print(len(trainData.shape)) 

# define the architecture of the network 
model = Sequential() 
# Long short term memory experiment 
model.add(TimeDistributed(LSTM(32, return_sequences=True),input_shape=trainData.shape[1:])) 
model.add(TimeDistributed(LSTM(10, return_sequences=True))) 
model.add(Activation("softmax")) 

print(trainData.shape) 
print(trainLabels.shape) 

## train the model using SGD 
print("[INFO] compiling model...") 
sgd = SGD(lr=0.01) 
model.compile(loss="sparse_categorical_crossentropy", optimizer=sgd, metrics=["accuracy"]) 
model.fit(trainData, trainLabels) 

## show the accuracy on the testing set 
print("[INFO] evaluating on testing set...") 
(loss, accuracy) = model.evaluate(testData, testLabels, batch_size=128, verbose=1) 
print("[INFO] loss={:.4f}, accuracy: {:.4f}%".format(loss, accuracy * 100)) 

Answer 1

當訪問元組，或者在Python（和大多數編程語言）陣列的計數從0開始，而不是1

因此，試圖訪問input_shape[2]實際上是input_shape的第三項。