Я хочу, чтобы создать простой автоассоциатор с 3000 входом, 2 скрытых и 3000 выходных нейронов:Попадая размеров неправильно при создании упреждающего автоассоциатор в Теано/Лазанье
def build_autoencoder(input_var=None):
l_in = InputLayer(shape=(None,3000), input_var=input_var)
l_hid = DenseLayer(
l_in, num_units=2,
nonlinearity=rectify,
W=lasagne.init.GlorotUniform())
l_out = DenseLayer(
l_hid, num_units=3000,
nonlinearity=softmax)
return l_out
формой обучающих данных выглядит следующим образом:
train.shape = (3000,3)
Это определение ввода, цель и функции потерь:
import sys
import os
import time
import numpy as np
import theano
import theano.tensor as T
import lasagne
from lasagne.updates import rmsprop
from lasagne.layers import DenseLayer, DropoutLayer, InputLayer
from lasagne.nonlinearities import rectify, softmax
from lasagne.objectives import categorical_crossentropy
# Creating the Theano variables
input_var = T.dmatrix('inputs')
target_var = T.dmatrix('targets')
# Building the Theano expressions on these variables
network = build_autoencoder(input_var)
prediction = lasagne.layers.get_output(network)
loss = categorical_crossentropy(prediction, target_var)
loss = loss.mean()
test_prediction = lasagne.layers.get_output(network,
deterministic=True)
test_loss = 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)
Я просто работает одна эпоха, но получаю сообщение об ошибке:
params = lasagne.layers.get_all_params(network, trainable=True)
updates = rmsprop(loss, params, learning_rate=0.001)
# Compiling the graph by declaring the Theano functions
train_fn = theano.function([input_var, target_var],
loss, updates=updates)
val_fn = theano.function([input_var, target_var],
[test_loss, test_acc])
# For loop that goes each time through the hole training
# and validation data
print("Starting training...")
for epoch in range(1):
# Going over the training data
train_err = 0
train_batches = 0
start_time = time.time()
print 'test1'
train_err += train_fn(train, train)
train_batches += 1
# Going over the validation data
val_err = 0
val_acc = 0
val_batches = 0
err, acc = val_fn(train, train)
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))
Это ошибка:
ValueError: ('shapes (3000,3) and (3000,2) not aligned: 3 (dim 1) != 3000 (dim 0)', (3000, 3), (3000, 2)) Apply node that caused the error: Dot22(inputs, W) Toposort index: 3 Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)] Inputs shapes: [(3000, 3), (3000, 2)] Inputs strides: [(24, 8), (16, 8)] Inputs values: ['not shown', 'not shown'] Outputs clients: [[Elemwise{add,no_inplace}(Dot22.0, InplaceDimShuffle{x,0}.0), Elemwise{Composite{(i0 * (Abs(i1) + i2 + i3))}}[(0, 2)](TensorConstant{(1, 1) of 0.5}, Elemwise{add,no_inplace}.0, Dot22.0, InplaceDimShuffle{x,0}.0)]]
Мне кажется, что узким местом автоматического кодера является проблемой. Есть идеи?