Mnist demo

.gitignore

@@ -3,4 +3,6 @@ build/
 *.user
 
 .vscode
-.idea
+.idea
+.project
+.pydevproject

demo/mnist/.gitignore

@@ -0,0 +1,6 @@
+data/raw_data
+data/*.list
+mnist_vgg_model
+plot.png
+train.log
+*pyc

demo/mnist/data/generate_list.py

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+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed 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.
+
+o = open("./" + "train.list", "w")
+o.write("./data/raw_data/train" +"\n")
+o.close()
+
+o = open("./" + "test.list", "w")
+o.write("./data/raw_data/t10k" +"\n")
+o.close()

demo/mnist/data/get_mnist_data.sh

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+#!/usr/bin/env sh
+# This scripts downloads the mnist data and unzips it.
+
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+rm -rf "$DIR/raw_data"
+mkdir "$DIR/raw_data"
+cd "$DIR/raw_data"
+
+echo "Downloading..."
+
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+    if [ ! -e $fname ]; then
+        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+        gunzip ${fname}.gz
+    fi
+done
+
+cd $DIR
+rm -f *.list
+python generate_list.py
+

demo/mnist/mnist_provider.py

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+from paddle.trainer.PyDataProvider2 import *
+
+
+# Define a py data provider
+@provider(input_types=[
+    dense_vector(28 * 28),
+    integer_value(10)
+])
+def process(settings, filename):  # settings is not used currently.
+    imgf = filename + "-images-idx3-ubyte"
+    labelf = filename + "-labels-idx1-ubyte"
+    f = open(imgf, "rb")
+    l = open(labelf, "rb")
+
+    f.read(16)
+    l.read(8)
+
+    # Define number of samples for train/test
+    if "train" in filename:
+        n = 60000
+    else:
+        n = 10000
+
+    for i in range(n):
+        label = ord(l.read(1))
+        pixels = []
+        for j in range(28*28):
+            pixels.append(float(ord(f.read(1))))
+        yield  { "pixel": pixels, 'label': label }
+
+    f.close()
+    l.close()
+

demo/mnist/train.sh

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+#!/bin/bash
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed 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.
+set -e
+config=vgg_16_mnist.py
+output=./mnist_vgg_model
+log=train.log
+
+paddle train \
+--config=$config \
+--dot_period=10 \
+--log_period=100 \
+--test_all_data_in_one_period=1 \
+--use_gpu=1 \
+--trainer_count=1 \
+--num_passes=100 \
+--save_dir=$output \
+2>&1 | tee $log
+
+python -m paddle.utils.plotcurve -i $log > plot.png

demo/mnist/vgg_16_mnist.py

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+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed 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.
+
+from paddle.trainer_config_helpers import *
+
+is_predict = get_config_arg("is_predict", bool, False)
+
+####################Data Configuration ##################
+
+
+if not is_predict:
+  data_dir='./data/'
+  define_py_data_sources2(train_list= data_dir + 'train.list',
+                        test_list= data_dir + 'test.list',
+                        module='mnist_provider',
+                        obj='process')
+
+######################Algorithm Configuration #############
+settings(
+    batch_size = 128,
+    learning_rate = 0.1 / 128.0,
+    learning_method = MomentumOptimizer(0.9),
+    regularization = L2Regularization(0.0005 * 128)
+)
+
+#######################Network Configuration #############
+
+data_size=1*28*28
+label_size=10
+img = data_layer(name='pixel', size=data_size)
+
+# small_vgg is predined in trainer_config_helpers.network
+predict = small_vgg(input_image=img,
+                    num_channels=1,
+                    num_classes=label_size)
+
+if not is_predict:
+    lbl = data_layer(name="label", size=label_size)
+    outputs(classification_cost(input=predict, label=lbl))
+else:
+    outputs(predict)