Python，Theano - ValueError：输入尺寸不匹配

Question

我基于 Lasagne 中的 mnist.py 示例在 Theano 中构建了一个 DNN。 我正在尝试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

这是里面用的

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。问题是我收到与数组维度相关的错误：

ValueError：输入维度不匹配。 (输入[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))} }(TensorConstant{(1, 1) of -1.0}, 目标, TensorConstant{(1, 1) of 1.0}, Elemwise{Add}[(0, 0)].0, Elemwise{mul,no_inplace}.0, Elemwise{sub,no_inplace}.0)

拓扑排序索引：17

输入类型：[TensorType(float64, (True, True)), TensorType(float64, matrix), TensorType(float64, (True, True)), TensorType(float64, matrix), TensorType(float64, (True, True)), TensorType(float64, matrix)]

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.]]), 'not shown', array([[ 30821.]]), 'not显示']

输出客户端：[[Dot22Scalar(InplaceDimShuffle{1,0}.0, Elemwise{Composite{(((i0 * i1 * (i2 - scalar_sigmoid(i3)))) / i4) - ((i0 * i5 * scalar_sigmoid (i3)) / i4))}}.0, TensorConstant{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)，目标的维度是 (30821, 1)。 我已经阅读了几页关于如何通过 reshape 修复此错误的内容，但它不适用于我的情况。 定义 target_var=T.matrix() 而不是 T.ivector() 也没有帮助。 为隐藏层设置一个合适的维度是可行的，但是这个神经网络的功能应该独立于这个数字。感谢您的帮助。

Answer 1

对于您的网络，输出为 256-dim。 由于您使用的是二元交叉熵损失函数，我想您想将样本分为 2 类。你需要一个 num_units=2 和 softmax 的输出层

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())
    l_output=lasagne.layers.DenseLayer(l_hidden1,num_units=2,nonlinearity=lasagne.nonlinearities.softmax,W=lasagne.init.GlorotUniform())

return l_output

这应该可行。如果有任何问题，请告诉我。