[GitHub] asitstands commented on a change in pull request #11268: A binary RBM example

[GitBox <gi...@apache.org>]

asitstands commented on a change in pull request #11268: A binary RBM example
URL: https://github.com/apache/incubator-mxnet/pull/11268#discussion_r209490146
 
 

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 File path: example/restricted-boltzmann-machine/binary_rbm_gluon.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import random as pyrnd
+import argparse
+import numpy as np
+import mxnet as mx
+from matplotlib import pyplot as plt
+from binary_rbm import BinaryRBMBlock
+from binary_rbm import estimate_log_likelihood
+
+
+### Helper function
+
+def get_non_auxiliary_params(rbm):
+    return rbm.collect_params('^(?!.*_aux_.*).*$')
+
+### Command line arguments
+
+parser = argparse.ArgumentParser(description='Restricted Boltzmann machine learning MNIST')
+parser.add_argument('--num-hidden', type=int, default=500, help='number of hidden units')
+parser.add_argument('--k', type=int, default=30, help='number of Gibbs sampling steps used in the PCD algorithm')
+parser.add_argument('--batch-size', type=int, default=80, help='batch size')
+parser.add_argument('--num-epoch', type=int, default=130, help='number of epochs')
+parser.add_argument('--learning-rate', type=float, default=0.1, help='learning rate for stochastic gradient descent') # The optimizer rescales this with `1 / batch_size`
+parser.add_argument('--momentum', type=float, default=0.3, help='momentum for the stochastic gradient descent')
+parser.add_argument('--ais-batch-size', type=int, default=100, help='batch size for AIS to estimate the log-likelihood')
+parser.add_argument('--ais-num-batch', type=int, default=10, help='number of batches for AIS to estimate the log-likelihood')
+parser.add_argument('--ais-intermediate-steps', type=int, default=10, help='number of intermediate distributions for AIS to estimate the log-likelihood')
+parser.add_argument('--ais-burn-in-steps', type=int, default=10, help='number of burn in steps for each intermediate distributions of AIS to estimate the log-likelihood')
+parser.add_argument('--cuda', action='store_true', dest='cuda', help='train on GPU with CUDA')
+parser.add_argument('--no-cuda', action='store_false', dest='cuda', help='train on CPU')
+parser.add_argument('--device-id', type=int, default=0, help='GPU device id')
+parser.add_argument('--data-loader-num-worker', type=int, default=4, help='number of multithreading workers for the data loader')
+parser.set_defaults(cuda=True)
+
+args = parser.parse_args()
+print(args)
+
+### Global environment
+
+mx.random.seed(pyrnd.getrandbits(32))
+ctx = mx.gpu(args.device_id) if args.cuda else mx.cpu()
+
+
+### Prepare data
+
+def data_transform(data, label):
+    return data.astype(np.float32) / 255, label.astype(np.float32)
+
+mnist_train_dataset = mx.gluon.data.vision.MNIST(train=True, transform=data_transform)
+mnist_test_dataset = mx.gluon.data.vision.MNIST(train=False, transform=data_transform)
+img_height = mnist_train_dataset[0][0].shape[0]
+img_width = mnist_train_dataset[0][0].shape[1]
+num_visible = img_width * img_height
+
+# This generates arrays with shape (batch_size, height = 28, width = 28, num_channel = 1)
+train_data = mx.gluon.data.DataLoader(mnist_train_dataset, args.batch_size, shuffle=True, num_workers=args.data_loader_num_worker)
+test_data = mx.gluon.data.DataLoader(mnist_test_dataset, args.batch_size, shuffle=True, num_workers=args.data_loader_num_worker)
+
+### Train
+
+rbm = BinaryRBMBlock(num_hidden=args.num_hidden, k=args.k, for_training=True, prefix='rbm_')
+rbm.initialize(mx.init.Normal(sigma=.01), ctx=ctx)
+rbm.hybridize()
+trainer = mx.gluon.Trainer(
+    get_non_auxiliary_params(rbm),
+    'sgd', {'learning_rate': args.learning_rate, 'momentum': args.momentum})
+for epoch in range(args.num_epoch):
+    # Update parameters
+    for batch, _ in train_data:
+        batch = batch.as_in_context(ctx).flatten()
+        with mx.autograd.record():
+            out = rbm(batch)
+        out[0].backward()
+        trainer.step(batch.shape[0])
+    mx.nd.waitall() # To restrict memory usage
+
+    # Monitor the performace of the model
+    params = get_non_auxiliary_params(rbm)
+    param_visible_layer_bias = params['rbm_visible_layer_bias'].data(ctx=ctx)
+    param_hidden_layer_bias = params['rbm_hidden_layer_bias'].data(ctx=ctx)
+    param_interaction_weight = params['rbm_interaction_weight'].data(ctx=ctx)
+    test_log_likelihood, _ = estimate_log_likelihood(
+            param_visible_layer_bias, param_hidden_layer_bias, param_interaction_weight,
+            args.ais_batch_size, args.ais_num_batch, args.ais_intermediate_steps, args.ais_burn_in_steps, test_data, ctx)
+    train_log_likelihood, _ = estimate_log_likelihood(
+            param_visible_layer_bias, param_hidden_layer_bias, param_interaction_weight,
+            args.ais_batch_size, args.ais_num_batch, args.ais_intermediate_steps, args.ais_burn_in_steps, train_data, ctx)
+    print("Epoch %d completed with test log-likelihood %f and train log-likelihood %f" % (epoch, test_log_likelihood, train_log_likelihood))
+
+
+### Show some samples.
+
+# Each sample is obtained by 3000 steps of Gibbs sampling starting from a real sample.
+# Starting from the real data is just for convenience of implmentation.
+# There must be no correlation between the initial states and the resulting samples.
 
 Review comment:
   Why irrelevant? I chose starting from real images insted of random images, but starting from a random images is also a common practice. Mentioning an alternative choice would be helpful here. No correlation between the initial state and the the steady state is also a general property of a Markov chain.

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