期望dense_1有2个维度，但有阵列形状（308,1,6）

我想第一次使用Conv1D多时间序列数据的多类分类和我的模型不断抛出此错误，当我使用它。期望dense_1有2个维度，但有阵列形状（308,1,6）

import numpy as np 
import os 

import keras 
from keras.models import Sequential 
from keras.layers import Conv1D, Dense, TimeDistributed, MaxPooling1D, Flatten 

# fix random seed for reproducibility 
np.random.seed(7) 

dataset1 = np.genfromtxt(os.path.join('data', 'norm_cellcycle_384_17.txt'), delimiter=',', dtype=None) 
data = dataset1[1:] 

# extract columns 
genes = data[:,0] 
y_all = data[:,1].astype(int) 
x_all = data[:,2:-1].astype(float) 

# deleted this line when using sparse_categorical_crossentropy 
# 384x6 
y_all = keras.utils.to_categorical(y_all) 

# 5 
num_classes = np.unique(y_all).shape[0] 

# split entire data into train set and test set 
validation_split = 0.2 

val_idx = np.random.choice(range(x_all.shape[0]), int(validation_split*x_all.shape[0]), replace=False) 
train_idx = [x for x in range(x_all.shape[0]) if x not in val_idx] 

x_train = x_all[train_idx] 
y_train = y_all[train_idx] 

# 308x17x1 
x_train = x_train[:, :, np.newaxis] 
# 308x1 
y_train = y_train[:,np.newaxis] 

x_test = x_all[val_idx] 
y_test = y_all[val_idx] 

# deleted this line when using sparse_categorical_crossentropy 
y_test = keras.utils.to_categorical(y_test) 

# 76x17x1 
x_test = x_test[:, :, np.newaxis] 
# 76x1 
y_test = y_test[:,np.newaxis] 


print(x_train.shape[0],'train samples') 
print(x_test.shape[0],'test samples') 


# Create Model 
# number of filters for 1D conv 
nb_filter = 4 
filter_length = 5 

window = x_train.shape[1] 
model = Sequential() 

model.add(Conv1D(filters=nb_filter,kernel_size=filter_length,activation="relu", input_shape=(window,1))) 
model.add(MaxPooling1D()) 
model.add(Conv1D(nb_filter=nb_filter, filter_length=filter_length, activation='relu')) 
model.add(MaxPooling1D()) 
model.add(Flatten()) 
model.add(Dense(num_classes, activation='softmax')) 
model.summary() 
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 

model.fit(x_train, y_train, epochs=25, batch_size=2, validation_data=(x_test, y_test))

我不知道为什么我得到这个错误。当我使用binary_crossentropy丢失和没有一个热门的编码y_all，我的模型的作品。但是，当我为y_all使用一个热门编码并且发生categorical_crossentropy丢失时，它会失败。当我不使用一个热门编码时，keras会抛出一个错误，让我将y_all更改为一个二进制矩阵。我甚至不知道（1,6）是从哪里来的。

ValueError: Error when checking model target: expected dense_1 to have 2 dimensions, but got array with shape (308, 1, 6)

请大家帮忙！我一直坚持这个好几个小时！已经经历了所有相关的问题，但仍然没有意义。

更新：我现在使用sparse_categorical_crossentropy，因为它具有整数支持。我删除从上面的代码中to_categorical线，我得到这个新的错误：

InvalidArgumentError (see above for traceback): Received a label value of 5 which is outside the valid range of [0, 5). Label values: 2 5
[[Node: SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_1, Cast)]]

请求的数据样本：

,Main,Gp,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c15,c16,c17 
YDL179w,1,-0.75808,-0.90319,-0.98935,-0.73995,-0.67193,-0.12777,-0.95307,-1.01656,0.79730,2.11688,1.98537,0.61591,0.56603,-0.13684,-0.52228,-0.05068,0.78823, 
YLR079w,1,-0.48845,-0.70828,-0.47688,-0.65814,-0.45374,-0.47302,-0.71214,-1.02839,0.24048,3.11376,1.28952,0.44874,0.04379,-0.31104,-0.30332,-0.34575,0.82285, 
YER111c,1,-0.42218,0.23887,1.84427,-0.02083,-0.61105,-0.65827,-0.79992,-0.39857,-0.09166,2.03314,1.58457,0.68744,0.14443,-0.72910,-1.46097,-0.82353,-0.51662, 
YBR200w,1,0.09824,0.55258,-0.89641,-1.19111,-1.11744,-0.76133,0.09824,2.16120,1.46126,1.03148,0.67537,-0.33155,-0.60170,-1.39987,-0.42978,-0.15963,0.81045, 
YPL209c,2,-0.65282,-0.32055,2.53702,2.00538,0.60982,0.51014,-0.55314,-1.01832,-0.78573,0.01173,0.07818,-0.05473,-0.22087,0.24432,-0.28732,-1.11801,-0.98510, 
YJL074c,2,-0.81087,-0.19448,1.72941,0.59002,-0.53069,-0.25051,-0.92294,-0.92294,-0.53069,0.08570,1.87884,1.97223,0.45927,-0.36258,-0.34390,-1.07237,-0.77351, 
YNL233w,2,-0.43997,0.66325,2.85098,0.74739,-0.42127,-0.47736,-0.79524,-0.80459,-0.48671,-0.21558,1.25226,1.01852,-0.10339,-0.56151,-0.96353,-0.46801,-0.79524, 
YLR313c,2,-0.46611,0.42952,3.01689,1.13856,0.01902,-0.44123,-0.66514,-0.98856,-0.59050,-0.47855,0.84002,0.39220,0.50416,-0.50342,-0.82685,-0.64026,-0.73977, 
YGR041w,2,-0.57187,-0.26687,1.10561,-0.38125,-0.68624,-0.26687,-0.87687,-1.18186,-0.80062,0.60999,2.09686,1.82998,1.14374,0.11437,-0.80062,-0.87687,-0.19062,

来源

2017-05-01 snazziii

请发布您的数据样本 – Pedia

@Pedia从我的数据中添加了一些行！ – snazziii

所以我注意到，即使我知道有5个班，这个数据集正如y_all获得的唯一值所看到的那样，由于某种原因，Keras to_categorical认为有6个类。

# 384x6 
y_all = keras.utils.to_categorical(y_all) 

# 5 
num_classes = np.unique(y_all).shape[0]

我不知道这是为什么。牢记这一点我改变了这行代码，我的模式开始运行：

model.add(Dense(num_classes, activation='softmax'))

到

model.add(Dense(num_classes+1, activation='softmax'))

我仍然不知道为什么to_categorical的行为这种方式。有人知道吗？

来源

2017-05-01 06:43:45 snazziii

我认为这只是看最大的整数值，并假设你有从0到这个数字的类 – maxymoo

0索引假设没有意义。尽管谢谢！ – snazziii

期望dense_1有2个维度，但有阵列形状（308,1,6）

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