Tidak dapat membuat jaringan autoencoder ini berfungsi dengan baik (dengan lapisan convolutional dan maxpool)

9

Jaringan Autoencoder tampaknya jauh lebih rumit daripada jaringan MLP classifier normal. Setelah beberapa upaya menggunakan Lasagne semua yang saya dapatkan dalam output yang direkonstruksi adalah sesuatu yang menyerupai yang terbaik, rata-rata buram dari semua gambar dari database MNIST tanpa perbedaan pada apa digit input sebenarnya.

Struktur jaringan yang saya pilih adalah lapisan kaskade berikut:

  1. lapisan input (28x28)
  2. Lapisan konvolusional 2D, ukuran filter 7x7
  3. Lapisan Max Pooling, ukuran 3x3, langkah 2x2
  4. Lapisan perataan padat (sepenuhnya terhubung), 10 unit (ini adalah bottleneck)
  5. Lapisan padat (terhubung penuh), 121 unit
  6. Membentuk kembali layer menjadi 11x11
  7. Lapisan konvolusional 2D, ukuran filter 3x3
  8. Faktor lapisan Peningkat 2D 2
  9. Lapisan konvolusional 2D, ukuran filter 3x3
  10. Faktor lapisan Peningkat 2D 2
  11. Lapisan konvolusional 2D, ukuran filter 5x5
  12. Feature max pooling (dari 31x28x28 hingga 28x28)

Semua lapisan konvolusional 2D memiliki bias yang tidak terikat, aktivasi sigmoid dan 31 filter.

Semua lapisan yang terhubung sepenuhnya memiliki aktivasi sigmoid.

Fungsi kerugian yang digunakan adalah kesalahan kuadrat , fungsi pembaruan adalah adagrad. Panjang potongan untuk pembelajaran adalah 100 sampel, dikalikan 1000 zaman.

Berikut ini adalah ilustrasi masalah: baris atas adalah beberapa sampel yang ditetapkan sebagai input jaringan, baris bawah adalah rekonstruksi:

input dan output autoencoder

Hanya untuk kelengkapan, berikut ini adalah kode yang saya gunakan:

import theano.tensor as T
import theano
import sys
sys.path.insert(0,'./Lasagne') # local checkout of Lasagne
import lasagne
from theano import pp
from theano import function
import gzip
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
def load_mnist():

    def load_mnist_images(filename):
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=16)
        # The inputs are vectors now, we reshape them to monochrome 2D images,
        # following the shape convention: (examples, channels, rows, columns)
        data = data.reshape(-1, 1, 28, 28)
        # The inputs come as bytes, we convert them to float32 in range [0,1].
        # (Actually to range [0, 255/256], for compatibility to the version
        # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)
        return data / np.float32(256)

    def load_mnist_labels(filename):
        # Read the labels in Yann LeCun's binary format.
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=8)
        # The labels are vectors of integers now, that's exactly what we want.
        return data

    X_train = load_mnist_images('train-images-idx3-ubyte.gz')
    y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')
    X_test = load_mnist_images('t10k-images-idx3-ubyte.gz')
    y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz')
    return X_train, y_train, X_test, y_test

def plot_filters(conv_layer):
    W = conv_layer.get_params()[0]
    W_fn = theano.function([],W)
    params = W_fn()
    ks = np.squeeze(params)
    kstack = np.vstack(ks)
    plt.imshow(kstack,interpolation='none')
    plt.show()

def main():

    #theano.config.exception_verbosity="high"
    #theano.config.optimizer='None'

    X_train, y_train, X_test, y_test = load_mnist()
    ohe = OneHotEncoder()

    y_train = ohe.fit_transform(np.expand_dims(y_train,1)).toarray()
    chunk_len = 100
    visamount = 10
    num_epochs = 1000
    num_filters=31
    dropout_p=.0
    print "X_train.shape",X_train.shape,"y_train.shape",y_train.shape
    input_var = T.tensor4('X')
    output_var = T.tensor4('X')
    conv_nonlinearity = lasagne.nonlinearities.sigmoid
    net = lasagne.layers.InputLayer((chunk_len,1,28,28), input_var)
    conv1 = net = lasagne.layers.Conv2DLayer(net,num_filters,(7,7),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.MaxPool2DLayer(net,(3,3),stride=(2,2))
    net = lasagne.layers.DropoutLayer(net,p=dropout_p)
    #conv2_layer = lasagne.layers.Conv2DLayer(dropout_layer,num_filters,(3,3),nonlinearity=conv_nonlinearity)
    #pool2_layer = lasagne.layers.MaxPool2DLayer(conv2_layer,(3,3),stride=(2,2))
    net = lasagne.layers.DenseLayer(net,10,nonlinearity=lasagne.nonlinearities.sigmoid)

    #augment_layer1 = lasagne.layers.DenseLayer(reduction_layer,33,nonlinearity=lasagne.nonlinearities.sigmoid)
    net = lasagne.layers.DenseLayer(net,121,nonlinearity=lasagne.nonlinearities.sigmoid)

    net = lasagne.layers.ReshapeLayer(net,(chunk_len,1,11,11))

    net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.Upscale2DLayer(net,2)

    net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
    #pool_after0 = lasagne.layers.MaxPool2DLayer(conv_after1,(3,3),stride=(2,2))
    net = lasagne.layers.Upscale2DLayer(net,2)

    net = lasagne.layers.DropoutLayer(net,p=dropout_p)

    #conv_after2 = lasagne.layers.Conv2DLayer(upscale_layer1,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
    #pool_after1 = lasagne.layers.MaxPool2DLayer(conv_after2,(3,3),stride=(1,1))
    #upscale_layer2 = lasagne.layers.Upscale2DLayer(pool_after1,4)

    net = lasagne.layers.Conv2DLayer(net,num_filters,(5,5),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.FeaturePoolLayer(net,num_filters,pool_function=theano.tensor.max)
    print "output_shape:",lasagne.layers.get_output_shape(net)
    params = lasagne.layers.get_all_params(net, trainable=True)
    prediction = lasagne.layers.get_output(net)
    loss = lasagne.objectives.squared_error(prediction, output_var)
    #loss = lasagne.objectives.binary_crossentropy(prediction, output_var)
    aggregated_loss = lasagne.objectives.aggregate(loss)
    updates = lasagne.updates.adagrad(aggregated_loss,params)
    train_fn = theano.function([input_var, output_var], loss, updates=updates)

    test_prediction = lasagne.layers.get_output(net, deterministic=True)
    predict_fn = theano.function([input_var], test_prediction)

    print "starting training..."
    for epoch in range(num_epochs):
        selected = list(set(np.random.random_integers(0,59999,chunk_len*4)))[:chunk_len]
        X_train_sub = X_train[selected,:]
        _loss = train_fn(X_train_sub, X_train_sub)
        print("Epoch %d: Loss %g" % (epoch + 1, np.sum(_loss) / len(X_train)))
        """
        chunk = X_train[0:chunk_len,:,:,:]
        result = predict_fn(chunk)
        vis1 = np.hstack([chunk[j,0,:,:] for j in range(visamount)])
        vis2 = np.hstack([result[j,0,:,:] for j in range(visamount)])
        plt.imshow(np.vstack([vis1,vis2]))
        plt.show()
        """
    print "done."

    chunk = X_train[0:chunk_len,:,:,:]
    result = predict_fn(chunk)
    print "chunk.shape",chunk.shape
    print "result.shape",result.shape
    plot_filters(conv1)
    for i in range(chunk_len/visamount):
        vis1 = np.hstack([chunk[i*visamount+j,0,:,:] for j in range(visamount)])
        vis2 = np.hstack([result[i*visamount+j,0,:,:] for j in range(visamount)])
        plt.imshow(np.vstack([vis1,vis2]))
        plt.show()
    import ipdb; ipdb.set_trace()

if __name__ == "__main__":
    main()

Ada ide tentang cara meningkatkan jaringan ini untuk mendapatkan autoencoder yang berfungsi dengan baik?

Masalah terpecahkan!

Dengan implementasi yang sangat berbeda, menggunakan penyearah bocor alih-alih fungsi sigmoid di lapisan convolutional, hanya 2 (!!) node di lapisan bottleneck dan konvolusi dengan 1x1 kernel di bagian paling akhir.

Berikut ini adalah hasil dari beberapa rekonstruksi:

masukkan deskripsi gambar di sini

Kode:

import theano.tensor as T
import theano
import sys
sys.path.insert(0,'./Lasagne') # local checkout of Lasagne
import lasagne
from theano import pp
from theano import function
import theano.tensor.nnet
import gzip
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
def load_mnist():

    def load_mnist_images(filename):
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=16)
        # The inputs are vectors now, we reshape them to monochrome 2D images,
        # following the shape convention: (examples, channels, rows, columns)
        data = data.reshape(-1, 1, 28, 28)
        # The inputs come as bytes, we convert them to float32 in range [0,1].
        # (Actually to range [0, 255/256], for compatibility to the version
        # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)
        return data / np.float32(256)

    def load_mnist_labels(filename):
        # Read the labels in Yann LeCun's binary format.
        with gzip.open(filename, 'rb') as f:
            data = np.frombuffer(f.read(), np.uint8, offset=8)
        # The labels are vectors of integers now, that's exactly what we want.
        return data

    X_train = load_mnist_images('train-images-idx3-ubyte.gz')
    y_train = load_mnist_labels('train-labels-idx1-ubyte.gz')
    X_test = load_mnist_images('t10k-images-idx3-ubyte.gz')
    y_test = load_mnist_labels('t10k-labels-idx1-ubyte.gz')
    return X_train, y_train, X_test, y_test

def main():

    X_train, y_train, X_test, y_test = load_mnist()
    ohe = OneHotEncoder()

    y_train = ohe.fit_transform(np.expand_dims(y_train,1)).toarray()
    chunk_len = 100
    num_epochs = 10000
    num_filters=7
    input_var = T.tensor4('X')
    output_var = T.tensor4('X')
    #conv_nonlinearity = lasagne.nonlinearities.sigmoid
    #conv_nonlinearity = lasagne.nonlinearities.rectify
    conv_nonlinearity = lasagne.nonlinearities.LeakyRectify(.1)
    softplus = theano.tensor.nnet.softplus
    #conv_nonlinearity = theano.tensor.nnet.softplus
    net = lasagne.layers.InputLayer((chunk_len,1,28,28), input_var)
    conv1 = net = lasagne.layers.Conv2DLayer(net,num_filters,(7,7),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.MaxPool2DLayer(net,(3,3),stride=(2,2))
    net = lasagne.layers.DenseLayer(net,2,nonlinearity=lasagne.nonlinearities.sigmoid)
    net = lasagne.layers.DenseLayer(net,49,nonlinearity=lasagne.nonlinearities.sigmoid)
    net = lasagne.layers.ReshapeLayer(net,(chunk_len,1,7,7))
    net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.MaxPool2DLayer(net,(3,3),stride=(1,1))
    net = lasagne.layers.Upscale2DLayer(net,4)
    net = lasagne.layers.Conv2DLayer(net,num_filters,(3,3),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.MaxPool2DLayer(net,(3,3),stride=(1,1))
    net = lasagne.layers.Upscale2DLayer(net,4)
    net = lasagne.layers.Conv2DLayer(net,num_filters,(5,5),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.Conv2DLayer(net,num_filters,(1,1),nonlinearity=conv_nonlinearity,untie_biases=True)
    net = lasagne.layers.FeaturePoolLayer(net,num_filters,pool_function=theano.tensor.max)
    net = lasagne.layers.Conv2DLayer(net,1,(1,1),nonlinearity=conv_nonlinearity,untie_biases=True)
    print "output shape:",net.output_shape
    params = lasagne.layers.get_all_params(net, trainable=True)
    prediction = lasagne.layers.get_output(net)
    loss = lasagne.objectives.squared_error(prediction, output_var)
    #loss = lasagne.objectives.binary_hinge_loss(prediction, output_var)
    aggregated_loss = lasagne.objectives.aggregate(loss)
    #updates = lasagne.updates.adagrad(aggregated_loss,params)
    updates = lasagne.updates.nesterov_momentum(aggregated_loss,params,0.5)#.005
    train_fn = theano.function([input_var, output_var], loss, updates=updates)

    test_prediction = lasagne.layers.get_output(net, deterministic=True)
    predict_fn = theano.function([input_var], test_prediction)

    print "starting training..."
    for epoch in range(num_epochs):
        selected = list(set(np.random.random_integers(0,59999,chunk_len*4)))[:chunk_len]
        X_train_sub = X_train[selected,:]
        _loss = train_fn(X_train_sub, X_train_sub)
        print("Epoch %d: Loss %g" % (epoch + 1, np.sum(_loss) / len(X_train)))
    print "done."

    chunk = X_train[0:chunk_len,:,:,:]
    result = predict_fn(chunk)
    print "chunk.shape",chunk.shape
    print "result.shape",result.shape
    visamount = 10
    for i in range(10):
        vis1 = np.hstack([chunk[i*visamount+j,0,:,:] for j in range(visamount)])
        vis2 = np.hstack([result[i*visamount+j,0,:,:] for j in range(visamount)])
        plt.imshow(np.vstack([vis1,vis2]))
        plt.show()

    import ipdb; ipdb.set_trace()
if __name__ == "__main__":
    main()
machine-learning  neural-networks  dimensionality-reduction  unsupervised-learning  autoencoders 
— fstab
sumber

Jawaban:

4

Anda mungkin mendapatkan lebih banyak wawasan dengan memvisualisasikan bobot, bukan hanya rekonstruksi. Saya memiliki masalah yang sama ketika bias saya salah konfigurasi. Semuanya di bawah ini ditulis berdasarkan pengalaman saya menulis perpustakaan belajar saya sendiri. Anda dapat melihat kode di sini di Github http://github.com/josephcatrambone/aij .

Ini adalah screenshot dari program saya ketika tidak ada bias. Ini hanya setelah mungkin sepuluh zaman karena saya sedang terburu-buru untuk menyelesaikan Langgan ini:

Hanya bobot - tidak bias.

Pembaruan berat dilakukan oleh operasi ini: 

weights.add_i(positiveProduct.subtract(negativeProduct).elementMultiply(learningRate / (float) batchSize));
//visibleBias.add_i(batch.subtract(negativeVisibleProbabilities).meanRow().elementMultiply(learningRate));
//hiddenBias.add_i(positiveHiddenProbabilities.subtract(negativeHiddenProbabilities).meanRow().elementMultiply(learningRate));

Jika saya batalkan komentar pada kode bias yang terlihat, saya mendapatkan hasil ini:

Bias yang terlihat benar.

Jika saya mengacaukan tanda kode bias yang terlihat (mengurangi bukannya menambahkan):

visibleBias.subtract_i(batch.subtract(negativeVisibleProbabilities).meanRow().elementMultiply(learningRate));

Saya mendapatkan gambar ini:

Tanda bias terbalik.

Bola salju mana dan akhirnya mencapai sesuatu seperti apa yang Anda miliki di atas. Periksa signage fungsi kesalahan Anda.

— Joseph Catrambone
sumber
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