Question

我正在尝试使用pymc3与贝叶斯建立神经网络。它由1个输入层，2个隐藏层和1个输出层组成。在每个隐藏层中，它都有5个节点。

当我尝试拥有的代码时，它返回一条错误消息，我不确定该如何解决。

这是代码

import os, sys
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import theano.tensor as tt

Input data

X = [1,2,3,4,....,2880]

Y = [2,3,4,5,....,2881]

Train/Test set

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2)

ann_input = theano.shared(np.array(X_train))

ann_output = theano.shared(np.array(Y_train))

Initialize random weights between each layer

init_1 = np.random.randn(X.shape[1], n_hidden).astype(float)

init_2 = np.random.randn(n_hidden, n_hidden).astype(float)

init_out = np.random.randn(n_hidden).astype(float)

with pm.Model() as neural_network:
 n_hidden = 5. ## nodes
# Weights from input to hidden layer
  weights_in_1 = pm.Normal('w_in_1', 0, sd=1,
                           shape=(X.shape[1], n_hidden),
                           testval=init_1)

# Weights from 1st to 2nd layer
  weights_1_2 = pm.Normal('w_1_2', 0, sd=1,
                          shape=(n_hidden, n_hidden),
                          testval=init_2)

# Weights from hidden layer to output
 weights_2_out = pm.Normal('w_2_out', 0, sd=1,
                           shape=(n_hidden,),
                           testval=init_out)

# Build neural-network using tanh activation function
 act_1 = pm.math.tanh(pm.math.dot(ann_input,
                                  weights_in_1))
 act_2 = pm.math.tanh(pm.math.dot(act_1,
                                  weights_1_2))
 act_out = pm.math.sigmoid(pm.math.dot(act_2,
                                  weights_2_out))

# Softmax
 p = tt.nnet.softmax(act_out)
 out = pm.Categorical('out', p=p, observed=ann_output)

我遇到了以下错误消息：

输入尺寸不匹配。 (input[0].shape[1] = 2880, input[1].shape[1] = 1)

这是整个错误消息：

File "/BNN/nn.py", line 65, in construct_nn
   out = pm.Categorical('out', p=p, observed=ann_output)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/pymc3/distributions/distribution.py", line 46, in __new__
   return model.Var(name, dist, data, total_size)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/pymc3/model.py", line 856, in Var
   total_size=total_size, model=self)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/pymc3/model.py", line 1389, in __init__
   self.logp_elemwiset = distribution.logp(data)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/pymc3/distributions/discrete.py", line 1006, in logp
   tt.all(p_ >= 0, axis=-1), tt.all(p <= 1, axis=-1))
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/pymc3/distributions/dist_math.py", line 50, in bound
   return tt.switch(alltrue(conditions), logp, -np.inf)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/theano/gof/op.py", line 674, in __call__
   required = thunk()
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/theano/gof/op.py", line 862, in rval
   thunk()
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/theano/gof/cc.py", line 1739, in __call__
   reraise(exc_type, exc_value, exc_trace)
 File "/Users/miniconda3/envs/bnn/lib/python3.6/site-packages/six.py", line 693, in reraise
   raise value
ValueError: Input dimension mis-match. (input[0].shape[1] = 2880, input[1].shape[1] = 1)```

如何解决此问题：输入尺寸不匹配？

0 个答案: