我根据Lasagne中的mnist.py示例在Theano中构建了一个DNN . 我试图首先训练由单个隐藏层构成的神经网络,定义为
def build_first_auto(input_var=None):
l_input=lasagne.layers.InputLayer(shape=(None, 1, 48, 1), input_var=input_var)
l_hidden1=lasagne.layers.DenseLayer(l_input,num_units=256,nonlinearity=lasagne.nonlinearities.sigmoid,W=lasagne.init.GlorotUniform())
return l_hidden1
这在里面使用
from load_dataset import load_dataset
from build_DNNs import build_first_auto
import sys
import os
import time
import numpy as np
from numpy import linalg as LA
import theano
import theano.tensor as T
import lasagne
import scipy.io as sio
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
def train_autoencoder(num_epochs):
Xtrain, ytrain = load_dataset()
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.matrix('targets')
# Create neural network model
network = build_first_auto(input_var)
prediction = lasagne.layers.get_output(network)
params = lasagne.layers.get_all_params(network, trainable=True)
loss = lasagne.objectives.binary_crossentropy(prediction, target_var)
loss = loss.mean()
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
np.save('params', params)
#Monitoring the training
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction,axis=1),target_var),dtype=theano.config.floatX)
#Compile
train_fn = theano.function([input_var, target_var], loss, updates=updates, on_unused_input='ignore' ) #on_unused_input='ignore'
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
#Training
print("Starting training...")
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(Xtrain, ytrain, 30821, shuffle=True):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(Xtrain, ytrain, 30821, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
print(" validation accuracy:\t\t{:.2f} %".format(
val_acc / val_batches * 100))
损失函数是二元交叉熵 . 问题是我收到了与数组维度相关的错误:
ValueError:输入维度不匹配 . (input [1] .shape [1] = 1,input [3] .shape [1] = 256)应用导致错误的节点:Elemwise {Composite {(((i0 * i1 *(i2-scalar_sigmoid(i3) ))/ i4) - ((i0 * i5 * scalar_sigmoid(i3))/ i4))}}(-1.0}的TensorConstant {(1,1),目标,1.0的TensorConstant {(1,1)},Elemwise [(0,0)] . 0,Elemwise {mul,no_inplace} .0,Elemwise {sub,no_inplace} .0)Toposort index:17输入类型:[TensorType(float64,(True,True)), TensorType(float64,矩阵),TensorType(float64,(True,True)),TensorType(float64,矩阵),TensorType(float64,(True,True)),TensorType(float64,矩阵)]输入形状:[(1, 1),(30821,1),(1,1),(30821,256),(1,1),(30821,1)]输入步幅:[(8,8),(8,8),( 8,8),(2048,8),(8,8),(8,8)]输入值:[array([[ - 1 . ]]),'not shown',array([[1,8] ]),'not shown',array([[30821.]]),'not shown']输出客户:[[Dot22Scalar(InplaceDimShuffle {1,0} .0,Elemwise {Composite {(((i0 * i1 * (i2-scalar_sigmoid(i3)))/ i4) - ((i0 * i5 * scalar_sigmoid(i3))/ i4))}} . 0,TensorCon stant {0.01}),Sum {axis = [0],acc_dtype = float64}(Elemwise {Composite {(((i0 * i1 *(i2-scalar_sigmoid(i3)))/ i4) - ((i0 * i5 * scalar_sigmoid (i3))/ i4))}} . 0)]]
作为提示我可以说输入的维度是(30821,1,48,1)和目标(30821,1) . 我已经阅读了几个关于如何使用reshape修复此错误的页面,但它对我的情况不起作用 . 定义target_var = T.matrix()而不是T.ivector()也无济于事 . 为隐藏层设置适当的维度是有效的,但是这个神经网络的功能应该由这个数字独立 . 谢谢你的帮助 .
1 回答
对于您的网络,输出为256-dim . 由于您使用的是二进制交叉熵损失函数,我想您希望将样本分为两类 . 您需要一个num_units = 2和softmax的输出层
这应该工作 . 如果有任何问题,请告诉我 .