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Posted to commits@singa.apache.org by ka...@apache.org on 2017/03/15 09:16:27 UTC
[1/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Repository: incubator-singa
Updated Branches:
refs/heads/master a6e7690bd -> 0695daa66
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/requirements.txt
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diff --git a/doc/en/docs/notebook/requirements.txt b/doc/en/docs/notebook/requirements.txt
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@@ -0,0 +1,5 @@
+matplotlib=2.0.0=np112py27_0
+nb_conda_kernels=2.0.0=py27_0
+nb_conda=2.0.0=py27_0
+pillow=4.0.0=py27_1
+tqdm=4.11.2=py27_0
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/rnn.ipynb
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diff --git a/doc/en/docs/notebook/rnn.ipynb b/doc/en/docs/notebook/rnn.ipynb
new file mode 100644
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+++ b/doc/en/docs/notebook/rnn.ipynb
@@ -0,0 +1,257 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# RNN for Character Level Language Modeling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dataset pre-processing\n",
+ "\n",
+ "### sample data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import cPickle as pickle\n",
+ "import numpy as np\n",
+ "import argparse\n",
+ "\n",
+ "# sys.path.append(os.path.join(os.path.dirname(__file__), '../../build/python'))\n",
+ "from singa import layer\n",
+ "from singa import loss\n",
+ "from singa import device\n",
+ "from singa import tensor\n",
+ "from singa import optimizer\n",
+ "from singa import initializer\n",
+ "from singa.proto import model_pb2\n",
+ "from tqdm import tnrange"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "class Data(object):\n",
+ "\n",
+ " def __init__(self, fpath, batch_size=32, seq_length=100, train_ratio=0.8):\n",
+ " '''Data object for loading a plain text file.\n",
+ " Args:\n",
+ " fpath, path to the text file.\n",
+ " train_ratio, split the text file into train and test sets, where\n",
+ " train_ratio of the characters are in the train set.\n",
+ " '''\n",
+ " self.raw_data = open(fpath, 'r').read() # read text file\n",
+ " chars = list(set(self.raw_data))\n",
+ " self.vocab_size = len(chars)\n",
+ " self.char_to_idx = {ch: i for i, ch in enumerate(chars)}\n",
+ " self.idx_to_char = {i: ch for i, ch in enumerate(chars)}\n",
+ " data = [self.char_to_idx[c] for c in self.raw_data]\n",
+ " # seq_length + 1 for the data + label\n",
+ " nsamples = len(data) / (1 + seq_length)\n",
+ " data = data[0:nsamples * (1 + seq_length)]\n",
+ " data = np.asarray(data, dtype=np.int32)\n",
+ " data = np.reshape(data, (-1, seq_length + 1))\n",
+ " # shuffle all sequences\n",
+ " np.random.shuffle(data)\n",
+ " self.train_dat = data[0:int(data.shape[0]*train_ratio)]\n",
+ " self.num_train_batch = self.train_dat.shape[0] / batch_size\n",
+ " self.val_dat = data[self.train_dat.shape[0]:]\n",
+ " self.num_test_batch = self.val_dat.shape[0] / batch_size\n",
+ " print 'train dat', self.train_dat.shape\n",
+ " print 'val dat', self.val_dat.shape\n",
+ " \n",
+ "def numpy2tensors(npx, npy, dev):\n",
+ " '''batch, seq, dim -- > seq, batch, dim'''\n",
+ " tmpx = np.swapaxes(npx, 0, 1)\n",
+ " tmpy = np.swapaxes(npy, 0, 1)\n",
+ " inputs = []\n",
+ " labels = []\n",
+ " for t in range(tmpx.shape[0]):\n",
+ " x = tensor.from_numpy(tmpx[t])\n",
+ " y = tensor.from_numpy(tmpy[t])\n",
+ " x.to_device(dev)\n",
+ " y.to_device(dev)\n",
+ " inputs.append(x)\n",
+ " labels.append(y)\n",
+ " return inputs, labels\n",
+ "\n",
+ "\n",
+ "def convert(batch, batch_size, seq_length, vocab_size, dev):\n",
+ " '''convert a batch of data into a sequence of input tensors'''\n",
+ " y = batch[:, 1:]\n",
+ " x1 = batch[:, :seq_length]\n",
+ " x = np.zeros((batch_size, seq_length, vocab_size), dtype=np.float32)\n",
+ " for b in range(batch_size):\n",
+ " for t in range(seq_length):\n",
+ " c = x1[b, t]\n",
+ " x[b, t, c] = 1\n",
+ " return numpy2tensors(x, y, dev)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create the network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "def get_lr(epoch):\n",
+ " return 0.001 / float(1 << (epoch / 50))\n",
+ "\n",
+ "data = Data('static/linux_input.txt')\n",
+ "# SGD with L2 gradient normalization\n",
+ "opt = optimizer.RMSProp(constraint=optimizer.L2Constraint(5))\n",
+ "cuda = device.create_cuda_gpu()\n",
+ "rnn = layer.LSTM(name='lstm', hidden_size=32, num_stacks=1, dropout=0.5, input_sample_shape=(data.vocab_size,))\n",
+ "rnn.to_device(cuda)\n",
+ "rnn_w = rnn.param_values()[0]\n",
+ "rnn_w.uniform(-0.08, 0.08) \n",
+ "\n",
+ "dense = layer.Dense('dense', data.vocab_size, input_sample_shape=(32,))\n",
+ "dense.to_device(cuda)\n",
+ "dense_w = dense.param_values()[0]\n",
+ "dense_b = dense.param_values()[1]\n",
+ "print 'dense w ', dense_w.shape\n",
+ "print 'dense b ', dense_b.shape\n",
+ "initializer.uniform(dense_w, dense_w.shape[0], 0)\n",
+ "print 'dense weight l1 = %f' % (dense_w.l1())\n",
+ "dense_b.set_value(0)\n",
+ "print 'dense b l1 = %f' % (dense_b.l1())\n",
+ "\n",
+ "g_dense_w = tensor.Tensor(dense_w.shape, cuda)\n",
+ "g_dense_b = tensor.Tensor(dense_b.shape, cuda)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conduct SGD"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "lossfun = loss.SoftmaxCrossEntropy()\n",
+ "train_loss = 0\n",
+ "for epoch in range(3):\n",
+ " bar = tnrange(data.num_train_batch, desc='Epoch %d' % 0)\n",
+ " for b in bar:\n",
+ " batch = data.train_dat[b * batch_size: (b + 1) * batch_size]\n",
+ " inputs, labels = convert(batch, batch_size, seq_length, data.vocab_size, cuda)\n",
+ " inputs.append(tensor.Tensor())\n",
+ " inputs.append(tensor.Tensor())\n",
+ "\n",
+ " outputs = rnn.forward(model_pb2.kTrain, inputs)[0:-2]\n",
+ " grads = []\n",
+ " batch_loss = 0\n",
+ " g_dense_w.set_value(0.0)\n",
+ " g_dense_b.set_value(0.0)\n",
+ " for output, label in zip(outputs, labels):\n",
+ " act = dense.forward(model_pb2.kTrain, output)\n",
+ " lvalue = lossfun.forward(model_pb2.kTrain, act, label)\n",
+ " batch_loss += lvalue.l1()\n",
+ " grad = lossfun.backward()\n",
+ " grad /= batch_size\n",
+ " grad, gwb = dense.backward(model_pb2.kTrain, grad)\n",
+ " grads.append(grad)\n",
+ " g_dense_w += gwb[0]\n",
+ " g_dense_b += gwb[1]\n",
+ " # print output.l1(), act.l1()\n",
+ " bar.set_postfix(train_loss=batch_loss / seq_length)\n",
+ " train_loss += batch_loss\n",
+ "\n",
+ " grads.append(tensor.Tensor())\n",
+ " grads.append(tensor.Tensor())\n",
+ " g_rnn_w = rnn.backward(model_pb2.kTrain, grads)[1][0]\n",
+ " dense_w, dense_b = dense.param_values()\n",
+ " opt.apply_with_lr(epoch, get_lr(epoch), g_rnn_w, rnn_w, 'rnnw')\n",
+ " opt.apply_with_lr(epoch, get_lr(epoch), g_dense_w, dense_w, 'dense_w')\n",
+ " opt.apply_with_lr(epoch, get_lr(epoch), g_dense_b, dense_b, 'dense_b')\n",
+ " print '\\nEpoch %d, train loss is %f' % (epoch, train_loss / data.num_train_batch / seq_length)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Checkpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "with open('%s_%d.bin' % (model_path, epoch), 'wb') as fd:\n",
+ " print 'saving model to %s' % model_path\n",
+ " d = {}\n",
+ " for name, w in zip(['rnn_w', 'dense_w', 'dense_b'],[rnn_w, dense_w, dense_b]):\n",
+ " d[name] = tensor.to_numpy(w)\n",
+ " d['idx_to_char'] = data.idx_to_char\n",
+ " d['char_to_idx'] = data.char_to_idx\n",
+ " d['hidden_size'] = hidden_size\n",
+ " d['num_stacks'] = num_stacks\n",
+ " d['dropout'] = dropout\n",
+ " pickle.dump(d, fd)"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:conda]",
+ "language": "python",
+ "name": "conda-env-conda-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/static/bp.PNG
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http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/static/singav1-sw.png
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http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/python/singa/layer.py
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diff --git a/python/singa/layer.py b/python/singa/layer.py
index 7975042..6302b45 100644
--- a/python/singa/layer.py
+++ b/python/singa/layer.py
@@ -27,7 +27,13 @@ Example usages::
# create a convolution layer
conv = layer.Conv2D('conv', 32, 3, 1, pad=1, input_sample_shape=(3, 32, 32))
+
+ # init param values
+ w, b = conv.param_values()
+ w.guassian(0, 0.01)
+ b.set_value(0)
conv.to_device(dev) # move the layer data onto a CudaGPU device
+
x = tensor.Tensor((3, 32, 32), dev)
x.uniform(-1, 1)
y = conv.foward(True, x)
@@ -766,8 +772,8 @@ class Merge(Layer):
Returns:
A list of replicated grad, one per source layer
'''
- assert isinstance(grad, tensor.Tensor), 'The input must be Tensor'\
- ' instead of %s' % type(grad).__name__
+ assert isinstance(grad, tensor.Tensor), 'The input must be Tensor' \
+ ' instead of %s' % type(grad).__name__
return [grad] * self.num_input, [] # * self.num_input
@@ -902,7 +908,8 @@ class Slice(Layer):
def get_output_sample_shape(self):
out = []
for i in range(len(self.conf.slice_conf.slice_point) + 1):
- out.append(self.layer.GetOutputSampleShape(i))
+ out.append(self.layer.GetOutputSampleShapeAt(i))
+ return out
def forward(self, flag, x):
'''Slice the input tensor on the given axis.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/python/singa/tensor.py
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diff --git a/python/singa/tensor.py b/python/singa/tensor.py
index 7dee9f5..82361c5 100644
--- a/python/singa/tensor.py
+++ b/python/singa/tensor.py
@@ -60,6 +60,8 @@ from .proto import core_pb2
from . import singa_wrap as singa
import device as pydevice
+int32 = core_pb2.kInt
+float32 = core_pb2.kFloat32
class Tensor(object):
'''Create a Py Tensor, which wraps a swig converted Tensor from CPP Tensor
[2/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/regression.ipynb
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diff --git a/doc/en/docs/notebook/regression.ipynb b/doc/en/docs/notebook/regression.ipynb
index 4e81a20..a61aed6 100755
--- a/doc/en/docs/notebook/regression.ipynb
+++ b/doc/en/docs/notebook/regression.ipynb
@@ -60,7 +60,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.legend.Legend at 0x7fce59cef510>"
+ "<matplotlib.legend.Legend at 0x7f3c7ebe0050>"
]
},
"execution_count": 3,
@@ -69,9 +69,9 @@
},
{
"data": {
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LTnme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\nOiX0cgAAAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fce59cef550>"
+ "<matplotlib.figure.Figure at 0x7f3cce2f9cd0>"
]
},
"metadata": {},
@@ -109,7 +109,7 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7fce43e79390>]"
+ "[<matplotlib.lines.Line2D at 0x7f3c7b71a410>]"
]
},
"execution_count": 4,
@@ -118,9 +118,9 @@
},
{
"data": {
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K15TkrvBP4WeGKawdWoynm5BTiYmf8BkJldPzdVzkkCl0REAFsoEv0Kq0d3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\npI77P/o8W9CbuMwEAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fce59e405d0>"
+ "<matplotlib.figure.Figure at 0x7f3c7ec4bcd0>"
]
},
"metadata": {},
@@ -142,14 +142,14 @@
"source": [
"## Training via SGD\n",
"\n",
- "Assuming that we know the training data points are sampled from a line, but we don't know the line slope and offset. The training is then to learn the slop (k) and intercept (b) by minimizing the error, i.e. ||kx+b-y||^2. \n",
+ "Assuming that we know the training data points are sampled from a line, but we don't know the line slope and intercept. The training is then to learn the slop (k) and intercept (b) by minimizing the error, i.e. ||kx+b-y||^2. \n",
"1. we set the initial values of k and b (could be any values).\n",
"2. we iteratively update k and b by moving them in the direction of reducing the prediction error, i.e. in the gradient direction. For every iteration, we plot the learned line."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 14,
"metadata": {
"collapsed": false
},
@@ -165,7 +165,7 @@
"\n",
"# set hyper-parameters\n",
"max_iter = 15\n",
- "alpha = 0.1\n",
+ "alpha = 0.05\n",
"\n",
"# init parameters\n",
"k, b = 2.,0."
@@ -187,7 +187,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 15,
"metadata": {
"collapsed": false
},
@@ -196,28 +196,28 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "8.4457921346\n",
- "6.52662099202\n",
- "5.04807383219\n",
- "3.90897369385\n",
- "3.03137512207\n",
- "2.35523325602\n",
- "1.83428827922\n",
- "1.43290456136\n",
- "1.12362861633\n",
- "0.885310490926\n",
- "0.701658376058\n",
- "0.560119374593\n",
- "0.451024500529\n",
- "0.366924413045\n",
- "0.3020805041\n"
+ "loss at iter 0 = 8.897375\n",
+ "loss at iter 1 = 7.806258\n",
+ "loss at iter 2 = 6.850243\n",
+ "loss at iter 3 = 6.012600\n",
+ "loss at iter 4 = 5.278670\n",
+ "loss at iter 5 = 4.635614\n",
+ "loss at iter 6 = 4.072176\n",
+ "loss at iter 7 = 3.578499\n",
+ "loss at iter 8 = 3.145944\n",
+ "loss at iter 9 = 2.766943\n",
+ "loss at iter 10 = 2.434863\n",
+ "loss at iter 11 = 2.143896\n",
+ "loss at iter 12 = 1.888950\n",
+ "loss at iter 13 = 1.665564\n",
+ "loss at iter 14 = 1.469831\n"
]
},
{
"data": {
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<TRUNCATED>
[4/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/model.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/model.ipynb b/doc/en/docs/notebook/model.ipynb
new file mode 100644
index 0000000..23a5553
--- /dev/null
+++ b/doc/en/docs/notebook/model.ipynb
@@ -0,0 +1,536 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# SINGA Model Classes\n",
+ "\n",
+ "<img src=\"http://singa.apache.org/en/_static/images/singav1-sw.png\" width=\"500px\"/>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Layer\n",
+ "\n",
+ "Typically, the life cycle of a layer instance includes:\n",
+ " 1. construct layer without input_sample_shapes, goto 2; or,\n",
+ " \n",
+ " construct layer with input_sample_shapes, goto 3;\n",
+ " 2. call setup to create the parameters and setup other meta fields;\n",
+ " 4. initialize the parameters of the layer\n",
+ " 3. call forward or access layer members\n",
+ " 4. call backward and get parameters for update"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from singa import tensor, device, layer\n",
+ "\n",
+ "#help(layer.Layer)\n",
+ "layer.engine='singacpp'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Common layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from singa.layer import Dense, Conv2D, MaxPooling2D, Activation, BatchNormalization, Softmax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dense Layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2, 3) (3,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "dense = Dense('dense', 3, input_sample_shape=(2,))\n",
+ "#dense.param_names()\n",
+ "w, b = dense.param_values()\n",
+ "print w.shape, b.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "w.gaussian(0, 0.1)\n",
+ "b.set_value(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.02440065, -0.03396009, 0.01396658],\n",
+ " [ 0.00771775, 0.07841966, -0.05931653]], dtype=float32)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x = tensor.Tensor((2,2))\n",
+ "x.uniform(-1, 1)\n",
+ "y = dense.forward(True, x)\n",
+ "tensor.to_numpy(y)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2, 2) (2, 3) (3,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "gx, [gw, gb] = dense.backward(True, y)\n",
+ "print gx.shape, gw.shape, gb.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Convolution Layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(4, 6, 6)\n"
+ ]
+ }
+ ],
+ "source": [
+ "conv = Conv2D('conv', 4, 3, 1, input_sample_shape=(3, 6, 6))\n",
+ "print conv.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Pooling Layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(4, 3, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "pool = MaxPooling2D('pool', 3, 2, input_sample_shape=(4, 6, 6))\n",
+ "print pool.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Branch layers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from singa.layer import Split, Merge, Slice, Concat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[(4, 6, 6), (4, 6, 6)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "split = Split('split', 2, input_sample_shape=(4, 6, 6))\n",
+ "print split.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(4, 6, 6)\n"
+ ]
+ }
+ ],
+ "source": [
+ "merge = Merge('merge', input_sample_shape=(4, 6, 6))\n",
+ "print merge.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[(2, 6, 6), (2, 6, 6)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "sli = Slice('slice', 1, [2], input_sample_shape=(4, 6, 6))\n",
+ "print sli.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(4, 6, 6)\n"
+ ]
+ }
+ ],
+ "source": [
+ "concat = Concat('concat', 1, input_sample_shapes=[(3, 6, 6), (1, 6, 6)])\n",
+ "print concat.get_output_sample_shape()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Metric and Loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.13973515 0.21827343 0.21068712 0.21905626 0.21224809]\n",
+ " [ 0.2354937 0.1047527 0.18490241 0.23617713 0.23867407]\n",
+ " [ 0.2659435 0.11397494 0.1659178 0.22683497 0.22732878]]\n",
+ "0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "from singa import metric\n",
+ "import numpy as np\n",
+ "\n",
+ "x = tensor.Tensor((3, 5))\n",
+ "x.uniform(0, 1) # randomly genearte the prediction activation\n",
+ "x = tensor.softmax(x) # normalize the prediction into probabilities\n",
+ "print tensor.to_numpy(x)\n",
+ "y = tensor.from_numpy(np.array([0, 1, 3], dtype=np.int)) # set the truth\n",
+ "\n",
+ "f = metric.Accuracy()\n",
+ "acc = f.evaluate(x, y) # averaged accuracy over all 3 samples in x\n",
+ "print acc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.80309379101\n",
+ "[[-0.78104687 0.18748793 0.16346708 0.24803984 0.18205206]\n",
+ " [ 0.21501946 -0.83683592 0.19003348 0.20714596 0.22463693]\n",
+ " [ 0.20000091 0.23285127 0.26842937 -0.87474263 0.17346108]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from singa import loss\n",
+ "\n",
+ "x = tensor.Tensor((3, 5))\n",
+ "x.uniform(0, 1) # randomly genearte the prediction activation\n",
+ "y = tensor.from_numpy(np.array([0, 1, 3], dtype=np.int)) # set the truth\n",
+ "\n",
+ "f = loss.SoftmaxCrossEntropy()\n",
+ "l = f.forward(True, x, y) # l is tensor with 3 loss values\n",
+ "g = f.backward() # g is a tensor containing all gradients of x w.r.t l\n",
+ "print l.l1()\n",
+ "print tensor.to_numpy(g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optimizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.tensor.Tensor at 0x7f6a0c7cfe90>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from singa import optimizer\n",
+ "\n",
+ "sgd = optimizer.SGD(lr=0.01, momentum=0.9, weight_decay=1e-4)\n",
+ "p = tensor.Tensor((3,5))\n",
+ "p.uniform(-1, 1)\n",
+ "g = tensor.Tensor((3,5))\n",
+ "g.gaussian(0, 0.01)\n",
+ "\n",
+ "sgd.apply(1, g, p, 'param') # use the global lr=0.1 for epoch 1\n",
+ "sgd.apply_with_lr(2, 0.03, g, p, 'param') # use lr=0.03 for epoch 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## FeedForwardNet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1 (32, 32, 32)\n",
+ "relu1 (32, 32, 32)\n",
+ "pool1 (32, 16, 16)\n",
+ "flat (8192,)\n",
+ "dense (10,)\n",
+ "[u'conv1_weight', u'conv1_bias', u'dense_weight', u'dense_bias']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from singa import net as ffnet\n",
+ "layer.engine = 'singacpp'\n",
+ "net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())\n",
+ "net.add(layer.Conv2D('conv1', 32, 5, 1, input_sample_shape=(3,32,32,)))\n",
+ "net.add(layer.Activation('relu1'))\n",
+ "net.add(layer.MaxPooling2D('pool1', 3, 2))\n",
+ "net.add(layer.Flatten('flat'))\n",
+ "net.add(layer.Dense('dense', 10))\n",
+ "\n",
+ "# init parameters\n",
+ "for p in net.param_values():\n",
+ " if len(p.shape) == 0:\n",
+ " p.set_value(0)\n",
+ " else:\n",
+ " p.gaussian(0, 0.01)\n",
+ "print net.param_names()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1 (32, 32, 32)\n",
+ "relu1 (32, 32, 32)\n",
+ "pool1 (32, 16, 16)\n",
+ "flat (8192,)\n",
+ "dense (10,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "layer.engine = 'cudnn'\n",
+ "net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())\n",
+ "net.add(layer.Conv2D('conv1', 32, 5, 1, input_sample_shape=(3,32,32,)))\n",
+ "net.add(layer.Activation('relu1'))\n",
+ "net.add(layer.MaxPooling2D('pool1', 3, 2))\n",
+ "net.add(layer.Flatten('flat'))\n",
+ "net.add(layer.Dense('dense', 10))\n",
+ "\n",
+ "# init parameters\n",
+ "for p in net.param_values():\n",
+ " if len(p.shape) == 0:\n",
+ " p.set_value(0)\n",
+ " else:\n",
+ " p.gaussian(0, 0.01)\n",
+ " \n",
+ "# move net onto gpu\n",
+ "dev = device.create_cuda_gpu()\n",
+ "net.to_device(dev)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "## Next: [Simple models](./regression.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:conda]",
+ "language": "python",
+ "name": "conda-env-conda-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
[5/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/mlp.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/mlp.ipynb b/doc/en/docs/notebook/mlp.ipynb
index ea21ee6..5dcbe28 100755
--- a/doc/en/docs/notebook/mlp.ipynb
+++ b/doc/en/docs/notebook/mlp.ipynb
@@ -41,7 +41,7 @@
"from singa import optimizer\n",
"from singa import loss\n",
"from singa import layer\n",
- "from singa.proto import model_pb2"
+ "#from singa.proto import model_pb2"
]
},
{
@@ -52,7 +52,7 @@
"\n",
"## Training data generation\n",
"\n",
- "The following thress steps would be conducted to generate the training data.\n",
+ "The following steps would be conducted to generate the training data.\n",
"1. draw a boundary line in the 2-d space \n",
"2. generate data points in the 2-dspace\n",
"3. label the data points above the boundary line as positive points, and label other points as negative points."
@@ -108,16 +108,16 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
- "<matplotlib.figure.Figure at 0x7fcfcf3b5e50>"
+ "<matplotlib.figure.Figure at 0x7f68af50c890>"
]
},
"metadata": {},
@@ -138,13 +138,6 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": []
- },
- {
- "cell_type": "markdown",
"metadata": {},
"source": [
"## Create the MLP model\n",
@@ -211,7 +204,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {
"collapsed": false
},
@@ -220,23 +213,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "training loss = 0.245654\n",
- "training loss = 0.236532\n",
- "training loss = 0.228489\n",
- "training loss = 0.221329\n",
- "training loss = 0.214903\n",
- "training loss = 0.209094\n",
- "training loss = 0.203810\n",
- "training loss = 0.198976\n",
- "training loss = 0.194531\n",
- "training loss = 0.190426\n"
+ "training loss = 0.705488\n",
+ "training loss = 0.479273\n",
+ "training loss = 0.414510\n",
+ "training loss = 0.370120\n",
+ "training loss = 0.337781\n",
+ "training loss = 0.313082\n",
+ "training loss = 0.293514\n",
+ "training loss = 0.277555\n",
+ "training loss = 0.264237\n",
+ "training loss = 0.252912\n"
]
},
{
"data": {
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<TRUNCATED>
[6/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/index.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/index.ipynb b/doc/en/docs/notebook/index.ipynb
index c252ba2..0c29187 100644
--- a/doc/en/docs/notebook/index.ipynb
+++ b/doc/en/docs/notebook/index.ipynb
@@ -4,47 +4,117 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ "\n",
+ "\n",
+ "# A Tutorial of SINGA V1\n",
+ "\n",
+ "Welcome to this tutorial for Apache Incubator-singa.\n",
+ "Please install [conda and SINGA](./installation.ipynb) before running the notebooks.\n",
+ "\n",
+ "## Outline\n",
+ "\n",
+ "* Introduction and installation\n",
+ "* SINGA classes\n",
+ " * [Core classes](./core.ipynb) - Tensor and Device\n",
+ " * [Model classes](./model.ipynb) - Layer, Loss, Metric, FeedForwardNet, Optimzier, Initializer\n",
+ "* Simple models \n",
+ " * [Linear Regression](./regression.ipynb)\n",
+ " * [Multi-layer Perceptron](./mlp.ipynb)\n",
+ "* Deep learning models\n",
+ " * [Convolutional Neural Network (CNN)](./cnn.ipynb)\n",
+ " * [Recurrent Neural Networks (RNN)](./rnn.ipynb) (WIP)\n",
+ " * [Restricted Boltzmann Machine (RBM)](./rbm.ipynb)\n",
+ "* [Distributed training](./distributed.ipynb) (WIP)\n",
+ "* [Rafiki](./rafiki.ipynb) (WIP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Welcome to this tutorial for Apache Incubator-singa using Jupyter Notebook.\n",
+ "## Introduction \n",
+ "### Training and Inference of deep learning models\n",
+ "\n",
+ "#### Training objective\n",
"\n",
- "Please install [PySINGA](http://singa.apache.org/en/docs/installation.html#install-pysinga) before running these tutorials.\n",
+ "$\\min_{\\theta} \\sum_{<x, y>\\in D}L(f(x|\\theta), y)$\n",
"\n",
- "1. [Regression](./regression.ipynb )\n",
+ "Training procedure\n",
+ "1. randomly initialize $\\theta$\n",
+ "2. get a mini-batch of data $\\{<x, y>\\}$ and compute $\\frac{\\partial L}{\\partial \\theta}$\n",
+ "3. $\\theta \\leftarrow \\theta - \\alpha \\frac{\\partial L}{\\partial \\theta}$\n",
+ "4. repeat 2-3 until converge, i.e., $||\\frac{\\partial L}{\\partial \\theta}|| < \\epsilon$ \n",
"\n",
- "2. [MLP Tutorial](./mlp.ipynb)\n",
+ "<img src=\"./static/sgd.png\" width=\"400px\"/>\n",
"\n",
- "3. [RBM Tutorial](./rbm.ipynb)\n",
+ "#### Back-propagation for gradient computation\n",
"\n",
+ "<img src=\"./static/bp.PNG\" width=\"300px\"/> \n",
"\n",
- "To learn more about Jupyter, please check [IPython in Depth](https://www.youtube.com/watch?v=xe_ATRmw0KM).\n",
+ "#### Popular deep learning models\n",
"\n",
- "If you want to use PySINGA and jupyter notebooks in virtual environment, please use conda virtual environment and install the following extension. Then you can select the kernel of the virtual environment in the browser. "
+ "<img src=\"./static/models.PNG\" width=\"200px\"/>\n",
+ "\n",
+ "\n",
+ "\n"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### SINGA V1\n",
+ "#### Software Stack\n",
+ "\n",
+ "<img src=\"http://singa.apache.org/en/_static/images/singav1-sw.png\" width=\"500px\"/>\n",
+ "\n",
+ "#### Progress\n",
+ "\n",
+ "|Components|Feature|Status|\n",
+ "|:---------|:------|:-----|\n",
+ "|Core |Tensor implementation|V1.0|\n",
+ "| |Device implementation (CppCPU, CudaGPU, OpenclGPU) |V1.1|\n",
+ "|Model|Basic layers for CNN and RNN| V1.0|\n",
+ "| |Basic loss and metric functions|V1.0|\n",
+ "| |Basic optimizer and initializers |V1.0|\n",
+ "|IO |Message paasing via socket| V1.0|\n",
+ "|Installation| From source on Linux and Mac|V1.0|\n",
+ "| | From source on Windows|V1.1|\n",
+ "| | From wheel on Linux and Mac|V1.0|\n",
+ "| | From debian package on Ubuntu |V1.1|\n",
+ "| | Docker images | V1.1|\n",
+ "| | AWS AMI | V1.1|\n",
+ "\n",
+ "#### Schedule\n",
+ "|Components|Feature|Status|\n",
+ "|:---------|:------|:-----|\n",
+ "|Core |Tensor API = Numpy API| V1.2|\n",
+ "| |Operation scheduling and memory optimization| V1.3|\n",
+ "|Model | To support more vison, NLP examples| V1.2|\n",
+ "| | General neural net API| V1.2|\n",
+ "| | To support reinforcement learning examples| V1.3|\n",
+ "| | Auto-grad + Functional API | TBD|\n",
+ "|IO | HDFS integration |V1.3|\n",
+ "|Server| Consistency and communication optimization |V1.2| \n",
+ "| |Message passing via Infiniband |V1.2| \n",
+ "|Installation | From wheel on Windows| V1.2|\n",
+ "| | From debian on Linux | V1.2|\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "conda install nb_conda_kernel"
+ "## Next: [SINGA core classes](./core.ipynb)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [default]",
+ "display_name": "Python [conda env:conda]",
"language": "python",
- "name": "python2"
+ "name": "conda-env-conda-py"
},
"language_info": {
"codemirror_mode": {
@@ -56,9 +126,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.13"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/installation.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/installation.ipynb b/doc/en/docs/notebook/installation.ipynb
new file mode 100644
index 0000000..65093dc
--- /dev/null
+++ b/doc/en/docs/notebook/installation.ipynb
@@ -0,0 +1,93 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The easiast way to install SINGA is via [conda](https://conda.io/docs/).\n",
+ "\n",
+ "## Install Conda\n",
+ "\n",
+ "If you haven't install conda, you can install it via\n",
+ "\n",
+ "* Linux 64\n",
+ "\n",
+ " wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh\n",
+ " bash miniconda.sh\n",
+ "\n",
+ "* Mac OSX\n",
+ "\n",
+ " wget https://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh miniconda.sh\n",
+ " bash miniconda.sh\n",
+ " \n",
+ "* Windows (not supported for the moment) \n",
+ "\n",
+ "## Install SINGA\n",
+ "\n",
+ "With Conda installed, you can install SINGA via\n",
+ "\n",
+ "* SINGA for Linux\n",
+ " * CPU Version\n",
+ " \n",
+ " conda install -c nusdbsystem singa\n",
+ " \n",
+ " * GPU Version\n",
+ " \n",
+ " conda install -c nusdbystem singa-cudax.y-cudnnz\n",
+ " \n",
+ " where <x,y,z> specifies the cuda and cuDNN version, which could be (cuda8.0, cudnn5), (cuda7.5, cudnn5) or (cuda7.5, cudnn4)\n",
+ " \n",
+ " \n",
+ "* SINGA for MacOSX (CPU only)\n",
+ " \n",
+ " conda config --add channels conda-forge\n",
+ " conda install -c nusdbsystem singa\n",
+ " \n",
+ " \n",
+ "## Install Jupyter Notebook and Other Packages\n",
+ "\n",
+ " conda install --file requirements.txt\n",
+ " \n",
+ "To learn more about Jupyter, please check [IPython in Depth](https://www.youtube.com/watch?v=xe_ATRmw0KM).\n",
+ "\n",
+ "\n",
+ "## Run the notebooks\n",
+ "\n",
+ "Download the [notebooks](https://github.com/apache/incubator-singa/tree/master/doc/en/docs/notebook) and start the jupyter\n",
+ "\n",
+ " jupyter notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:conda]",
+ "language": "python",
+ "name": "conda-env-conda-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
[8/8] incubator-singa git commit: Merge PR #314
Posted by ka...@apache.org.
Merge PR #314
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/0695daa6
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/0695daa6
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/0695daa6
Branch: refs/heads/master
Commit: 0695daa6626e111e47227d37e6583d19dedf735c
Parents: a6e7690 5144bcf
Author: kaiping <ka...@comp.nus.edu.sg>
Authored: Wed Mar 15 17:02:07 2017 +0800
Committer: kaiping <ka...@comp.nus.edu.sg>
Committed: Wed Mar 15 17:02:07 2017 +0800
----------------------------------------------------------------------
doc/en/docs/installation.md | 4 +-
doc/en/docs/notebook/cnn.ipynb | 907 ++++++++++++++++++
doc/en/docs/notebook/core.ipynb | 1151 +++++++++++++++++++++++
doc/en/docs/notebook/index.ipynb | 108 ++-
doc/en/docs/notebook/installation.ipynb | 93 ++
doc/en/docs/notebook/mlp.ipynb | 98 +-
doc/en/docs/notebook/model.ipynb | 536 +++++++++++
doc/en/docs/notebook/rbm.ipynb | 79 +-
doc/en/docs/notebook/regression.ipynb | 70 +-
doc/en/docs/notebook/requirements.txt | 5 +
doc/en/docs/notebook/rnn.ipynb | 257 +++++
doc/en/docs/notebook/static/bp.PNG | Bin 0 -> 22579 bytes
doc/en/docs/notebook/static/digit.jpg | Bin 0 -> 4746 bytes
doc/en/docs/notebook/static/models.PNG | Bin 0 -> 43188 bytes
doc/en/docs/notebook/static/sgd.png | Bin 0 -> 18725 bytes
doc/en/docs/notebook/static/singav1-sw.png | Bin 0 -> 24326 bytes
python/singa/layer.py | 13 +-
python/singa/tensor.py | 2 +
18 files changed, 3180 insertions(+), 143 deletions(-)
----------------------------------------------------------------------
[7/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
SINGA-305 - Add jupyter notebooks for SINGA V1 tutorial
Add notebooks for tensor, device, model classes; and mlp, linear regression, cnn, rbm examples;
TODO RNN examples, and distributed training, Rafiki.
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/5144bcf1
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/5144bcf1
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/5144bcf1
Branch: refs/heads/master
Commit: 5144bcf1a4c970f9165d9a164f23686eceaba749
Parents: 0be3e03
Author: wangwei <wa...@comp.nus.edu.sg>
Authored: Tue Mar 14 18:23:59 2017 +0800
Committer: wangwei <wa...@comp.nus.edu.sg>
Committed: Wed Mar 15 16:15:43 2017 +0800
----------------------------------------------------------------------
doc/en/docs/installation.md | 4 +-
doc/en/docs/notebook/cnn.ipynb | 907 ++++++++++++++++++
doc/en/docs/notebook/core.ipynb | 1151 +++++++++++++++++++++++
doc/en/docs/notebook/index.ipynb | 108 ++-
doc/en/docs/notebook/installation.ipynb | 93 ++
doc/en/docs/notebook/mlp.ipynb | 98 +-
doc/en/docs/notebook/model.ipynb | 536 +++++++++++
doc/en/docs/notebook/rbm.ipynb | 79 +-
doc/en/docs/notebook/regression.ipynb | 70 +-
doc/en/docs/notebook/requirements.txt | 5 +
doc/en/docs/notebook/rnn.ipynb | 257 +++++
doc/en/docs/notebook/static/bp.PNG | Bin 0 -> 22579 bytes
doc/en/docs/notebook/static/digit.jpg | Bin 0 -> 4746 bytes
doc/en/docs/notebook/static/models.PNG | Bin 0 -> 43188 bytes
doc/en/docs/notebook/static/sgd.png | Bin 0 -> 18725 bytes
doc/en/docs/notebook/static/singav1-sw.png | Bin 0 -> 24326 bytes
python/singa/layer.py | 13 +-
python/singa/tensor.py | 2 +
18 files changed, 3180 insertions(+), 143 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/installation.md
----------------------------------------------------------------------
diff --git a/doc/en/docs/installation.md b/doc/en/docs/installation.md
index 7772cc0..3a596d6 100755
--- a/doc/en/docs/installation.md
+++ b/doc/en/docs/installation.md
@@ -9,11 +9,11 @@ Currently, SINGA has conda packages (Python2.7) for Linux and MacOSX.
1. CPU only
- conda install -c nusdbsystem singa
+ conda install -c nusdbsystem singa
2. GPU via CUDA+cuDNN
- conda install -c nusdbsystem singa-cudax.y-cudnnz
+ conda install -c nusdbsystem singa-cudax.y-cudnnz
where `x.y,z` is one of <8.0, 5>, <7.5, 5> and <7.5, 4>.
Users need to install CUDA and cuDNN before installing SINGA.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/cnn.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/cnn.ipynb b/doc/en/docs/notebook/cnn.ipynb
new file mode 100644
index 0000000..64a3a25
--- /dev/null
+++ b/doc/en/docs/notebook/cnn.ipynb
@@ -0,0 +1,907 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Classify images from MNIST using LeNet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dataset\n",
+ "\n",
+ "Download the [dataset](http://deeplearning.net/data/mnist/mnist.pkl.gz) to your workspace (i.e. the notebook folder)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import cPickle, gzip\n",
+ "\n",
+ "# Load the dataset\n",
+ "f = gzip.open('mnist.pkl.gz', 'rb')\n",
+ "train_set, valid_set, _ = cPickle.load(f)\n",
+ "f.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(50000, 784) (50000,)\n",
+ "(10000, 784) (10000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print train_set[0].shape, train_set[1].shape\n",
+ "print valid_set[0].shape, valid_set[1].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "train_x = np.reshape(train_set[0], (50000, 1, 28, 28)).astype(np.float32, copy=False)\n",
+ "train_y = np.array(train_set[1]).astype(np.int32, copy=False)\n",
+ "valid_x = np.reshape(valid_set[0], (10000, 1, 28, 28))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f0747e21410>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbF0eDQFojFqf9i93931mdoKkB83sZ+7+yPgVsj8K6
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2R9IKNd/swzskrc1ur5V0f4G9\n/JZmmbm53MzSKvjYNd2M1+7e8B9Jl2jsHf+XJf1tET2U6etkSU9mP88U3ZukOzX2NHBYY++NXCXp\nw5K6Jb0o6SFJ7U3U23clPSVpt8aC1llQb8s19pR+t6Rd2c8lRR+7RF+FHDc+4QcExRt+QFCEHwiK\n8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8DZI6NXofNrQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f077003c810>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.imshow(train_x[0][0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create the CNN model\n",
+ "\n",
+ "TODO: plot the net structure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1 (32, 14, 14)\n",
+ "relu1 (32, 14, 14)\n",
+ "conv2 (32, 7, 7)\n",
+ "relu2 (32, 7, 7)\n",
+ "pool (32, 4, 4)\n",
+ "flat (512,)\n",
+ "dense (10,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<singa.layer.Dense at 0x7f0735355990>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from singa import net as ffnet\n",
+ "from singa.layer import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense\n",
+ "from singa import optimizer, loss, metric\n",
+ "from singa import layer\n",
+ "layer.engine = 'singacpp'\n",
+ "net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())\n",
+ "net.add(Conv2D('conv1', 32, 3, 2, input_sample_shape=(1,28,28)))\n",
+ "net.add(Activation('relu1'))\n",
+ "net.add(Conv2D('conv2', 32, 3, 2))\n",
+ "net.add(Activation('relu2'))\n",
+ "net.add(MaxPooling2D('pool', 3, 2))\n",
+ "net.add(Flatten('flat'))\n",
+ "net.add(Dense('dense', 10))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialize the parameters\n",
+ "\n",
+ "* weight matrix - guassian distribution\n",
+ "* bias - 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1_weight (32, 9) 0.0764843672514\n",
+ "conv1_bias (32,) 0.0\n",
+ "conv2_weight (32, 288) 0.0803024768829\n",
+ "conv2_bias (32,) 0.0\n",
+ "dense_weight (512, 10) 0.0795410946012\n",
+ "dense_bias (10,) 0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "for pname, pval in zip(net.param_names(), net.param_values()):\n",
+ " if len(pval.shape) > 1:\n",
+ " pval.gaussian(0, 0.1)\n",
+ " else:\n",
+ " pval.set_value(0)\n",
+ " print pname, pval.shape, pval.l1()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set up the optimizer and tensors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from singa import tensor\n",
+ "#from singa.proto import core_pb2\n",
+ "from singa import device\n",
+ "from singa import utils\n",
+ "cpu = device.get_default_device()\n",
+ "\n",
+ "opt = optimizer.SGD(momentum=0.9, weight_decay=1e-4)\n",
+ "batch_size = 32\n",
+ "num_train_batch = train_x.shape[0] / batch_size\n",
+ "\n",
+ "tx = tensor.Tensor((batch_size, 1, 28, 28))\n",
+ "ty = tensor.Tensor((batch_size,), cpu , tensor.int32)\n",
+ "\n",
+ "# for progress bar\n",
+ "from tqdm import tnrange\n",
+ "idx = np.arange(train_x.shape[0], dtype=np.int32)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conduct SGD\n",
+ "\n",
+ "1. process the training data multile time, each time is called on epoch; \n",
+ "2. for each epoch, read the data as mini-batches in random order\n",
+ "3. for each mini-batch, do BP and update the parameters "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Epoch = 0, training loss = 0.292792, training accuracy = 0.906530\n",
+ "\n",
+ "Epoch = 1, training loss = 0.109958, training accuracy = 0.965589\n"
+ ]
+ }
+ ],
+ "source": [
+ "for epoch in range(2):\n",
+ " np.random.shuffle(idx)\n",
+ " loss, acc = 0.0, 0.0\n",
+ " \n",
+ " bar = tnrange(num_train_batch, desc='Epoch %d' % epoch)\n",
+ " for b in bar:\n",
+ " x = train_x[idx[b * batch_size: (b + 1) * batch_size]]\n",
+ " y = train_y[idx[b * batch_size: (b + 1) * batch_size]]\n",
+ " tx.copy_from_numpy(x)\n",
+ " ty.copy_from_numpy(y)\n",
+ " grads, (l, a) = net.train(tx, ty)\n",
+ " loss += l\n",
+ " acc += a\n",
+ " for (s, p, g) in zip(net.param_names(), net.param_values(), grads):\n",
+ " opt.apply_with_lr(epoch, 0.01, g, p, str(s), b)\n",
+ " # update progress bar\n",
+ " bar.set_postfix(train_loss=l, train_accuracy=a)\n",
+ " print 'Epoch = %d, training loss = %f, training accuracy = %f' % (epoch, loss / num_train_batch, acc / num_train_batch) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Save model to disk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "net.save('checkpoint')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load model from disk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "NOTE: If your model was saved using pickle, then set use_pickle=True for loading it\n",
+ "conv2_bias\n",
+ "conv2_weight\n",
+ "dense_weight\n",
+ "conv1_bias\n",
+ "dense_bias\n",
+ "conv1_weight\n"
+ ]
+ }
+ ],
+ "source": [
+ "for pval in net.param_values():\n",
+ " pval.set_value(0)\n",
+ "net.load('checkpoint')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Do prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from PIL import Image\n",
+ "img = Image.open('static/digit.jpg').convert('L')\n",
+ "img = img.resize((28,28))\n",
+ "img = np.array(img, dtype=np.float32)/255\n",
+ "img = tensor.from_numpy(img)\n",
+ "img.reshape((1,1,28,28))\n",
+ "y=net.predict(img)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f07356e4210>]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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XhqMBbF6YkEr9zpTdxcub8AIC0ip0UkDuBRAM/VHyAiJwE8C+BXVDXvfZlE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\n
pwDKL9pIHRnC6PCA36X4IjkYw/TR4Z12UAonhjsFTs6yQzve7sim2DETdgx3CpRyRTG/VEQ2pG2Q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
AKI4U5EFED9Gu7n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\nAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f0747e45890>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "prob=tensor.to_numpy(y)[0]\n",
+ "plt.plot(range(10), prob)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Debug\n",
+ "\n",
+ "Print l1 norm or parameter and layer feature\n",
+ "\n",
+ "1. parameter initialization\n",
+ "2. learning rate\n",
+ "3. weight decay\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conv1_weight (32, 9) 7.97165679932\n",
+ "conv1_bias (32,) 0.0\n",
+ "conv2_weight (32, 288) 8.00566577911\n",
+ "conv2_bias (32,) 0.0\n",
+ "dense_weight (512, 10) 7.92119693756\n",
+ "dense_bias (10,) 0.0\n",
+ "\n",
+ "\n",
+ "Epoch 0\n",
+ "-->conv1: 3.886751\n",
+ "conv1-->relu1: 2.299547\n",
+ "relu1-->conv2: 603.797852\n",
+ "conv2-->relu2: 295.446167\n",
+ "relu2-->pool: 955.442017\n",
+ "pool-->flat: 955.442017\n",
+ "flat-->dense: 284901.062500\n",
+ "-->dense: 0.311904\n",
+ "dense-->flat: 0.311904\n",
+ "flat-->pool: 0.086828\n",
+ "pool-->relu2: 0.050518\n",
+ "relu2-->conv2: 9.024708\n",
+ "conv2-->relu1: 2.790344\n",
+ "relu1-->conv1: 336.182007\n",
+ "\n",
+ " loss = 79.148743, params\n",
+ "conv1_weight 10.1786603928\n",
+ "conv1_bias 12.2705039978\n",
+ "conv2_weight 8.05525493622\n",
+ "conv2_bias 0.13293106854\n",
+ "dense_weight 8.18883609772\n",
+ "dense_bias 0.00134002871346\n",
+ "\n",
+ "\n",
+ "Epoch 1\n",
+ "-->conv1: 20.362625\n",
+ "conv1-->relu1: 4.780766\n",
+ "relu1-->conv2: 2066.139404\n",
+ "conv2-->relu2: 488.272614\n",
+ "relu2-->pool: 1144.980225\n",
+ "pool-->flat: 1144.980225\n",
+ "flat-->dense: 1392742.250000\n",
+ "-->dense: 0.286131\n",
+ "dense-->flat: 0.286131\n",
+ "flat-->pool: 0.084796\n",
+ "pool-->relu2: 0.034586\n",
+ "relu2-->conv2: 7.695237\n",
+ "conv2-->relu1: 2.117975\n",
+ "relu1-->conv1: 487.341522\n",
+ "\n",
+ " loss = 70.960945, params\n",
+ "conv1_weight 16.7161483765\n",
+ "conv1_bias 34.0985298157\n",
+ "conv2_weight 8.74045848846\n",
+ "conv2_bias 0.33584010601\n",
+ "dense_weight 9.48363685608\n",
+ "dense_bias 0.00340566551313\n",
+ "\n",
+ "\n",
+ "Epoch 2\n",
+ "-->conv1: 52.302490\n",
+ "conv1-->relu1: 10.072969\n",
+ "relu1-->conv2: 12706.870117\n",
+ "conv2-->relu2: 1381.310059\n",
+ "relu2-->pool: 1888.169067\n",
+ "pool-->flat: 1888.169067\n",
+ "flat-->dense: 4740897.500000\n",
+ "-->dense: 0.350905\n",
+ "dense-->flat: 0.350905\n",
+ "flat-->pool: 0.111108\n",
+ "pool-->relu2: 0.023244\n",
+ "relu2-->conv2: 7.156909\n",
+ "conv2-->relu1: 1.548402\n",
+ "relu1-->conv1: 516.079651\n",
+ "\n",
+ " loss = 73.690216, params\n",
+ "conv1_weight 30.0410804749\n",
+ "conv1_bias 60.256187439\n",
+ "conv2_weight 11.2280254364\n",
+ "conv2_bias 0.536676049232\n",
+ "dense_weight 13.0333833694\n",
+ "dense_bias 0.00553257204592\n",
+ "\n",
+ "\n",
+ "Epoch 3\n",
+ "-->conv1: 96.670937\n",
+ "conv1-->relu1: 2.220010\n",
+ "relu1-->conv2: 16051.786133\n",
+ "conv2-->relu2: 705.551208\n",
+ "relu2-->pool: 846.462280\n",
+ "pool-->flat: 846.462280\n",
+ "flat-->dense: 4615773.000000\n",
+ "-->dense: 0.454912\n",
+ "dense-->flat: 0.454912\n",
+ "flat-->pool: 0.146553\n",
+ "pool-->relu2: 0.012394\n",
+ "relu2-->conv2: 6.071361\n",
+ "conv2-->relu1: 0.411632\n",
+ "relu1-->conv1: 328.999054\n",
+ "\n",
+ " loss = 81.878014, params\n",
+ "conv1_weight 44.8700485229\n",
+ "conv1_bias 111.767730713\n",
+ "conv2_weight 14.0987415314\n",
+ "conv2_bias 0.919560790062\n",
+ "dense_weight 18.0457553864\n",
+ "dense_bias 0.00845981575549\n",
+ "\n",
+ "\n",
+ "Epoch 4\n",
+ "-->conv1: 165.898941\n",
+ "conv1-->relu1: 0.000018\n",
+ "relu1-->conv2: 1.004575\n",
+ "conv2-->relu2: 0.049885\n",
+ "relu2-->pool: 0.214831\n",
+ "pool-->flat: 0.214831\n",
+ "flat-->dense: 1963.049194\n",
+ "-->dense: 0.560196\n",
+ "dense-->flat: 0.560196\n",
+ "flat-->pool: 0.182800\n",
+ "pool-->relu2: 0.026711\n",
+ "relu2-->conv2: 10.339769\n",
+ "conv2-->relu1: 0.006092\n",
+ "relu1-->conv1: 11.064970\n",
+ "\n",
+ " loss = 45.199768, params\n",
+ "conv1_weight 58.6796875\n",
+ "conv1_bias 162.814819336\n",
+ "conv2_weight 16.8376598358\n",
+ "conv2_bias 1.47756028175\n",
+ "dense_weight 23.0595436096\n",
+ "dense_bias 0.01112665236\n",
+ "\n",
+ "\n",
+ "Epoch 5\n",
+ "-->conv1: 241.562866\n",
+ "conv1-->relu1: 0.000000\n",
+ "relu1-->conv2: 1.477560\n",
+ "conv2-->relu2: 0.000000\n",
+ "relu2-->pool: 0.000000\n",
+ "pool-->flat: 0.000000\n",
+ "flat-->dense: 0.011127\n",
+ "-->dense: 0.668576\n",
+ "dense-->flat: 0.668576\n",
+ "flat-->pool: 0.218311\n",
+ "pool-->relu2: 0.000000\n",
+ "relu2-->conv2: 0.000000\n",
+ "conv2-->relu1: 0.000000\n",
+ "relu1-->conv1: 0.000000\n",
+ "\n",
+ " loss = 2.299366, params\n",
+ "conv1_weight 71.2618331909\n",
+ "conv1_bias 208.757034302\n",
+ "conv2_weight 19.3444824219\n",
+ "conv2_bias 2.00746393204\n",
+ "dense_weight 27.6571712494\n",
+ "dense_bias 0.0137531962246\n",
+ "\n",
+ "\n",
+ "Epoch 6\n",
+ "-->conv1: 296.653534\n",
+ "conv1-->relu1: 0.000000\n",
+ "relu1-->conv2: 2.007464\n",
+ "conv2-->relu2: 0.000000\n",
+ "relu2-->pool: 0.000000\n",
+ "pool-->flat: 0.000000\n",
+ "flat-->dense: 0.013753\n",
+ "-->dense: 0.910015\n",
+ "dense-->flat: 0.910015\n",
+ "flat-->pool: 0.297148\n",
+ "pool-->relu2: 0.000000\n",
+ "relu2-->conv2: 0.000000\n",
+ "conv2-->relu1: 0.000000\n",
+ "relu1-->conv1: 0.000000\n",
+ "\n",
+ " loss = 2.301979, params\n",
+ "conv1_weight 82.6378097534\n",
+ "conv1_bias 250.104797363\n",
+ "conv2_weight 21.6229515076\n",
+ "conv2_bias 2.484375\n",
+ "dense_weight 31.8290367126\n",
+ "dense_bias 0.0165074821562\n",
+ "\n",
+ "\n",
+ "Epoch 7\n",
+ "-->conv1: 348.356262\n",
+ "conv1-->relu1: 0.000000\n",
+ "relu1-->conv2: 2.484375\n",
+ "conv2-->relu2: 0.000000\n",
+ "relu2-->pool: 0.000000\n",
+ "pool-->flat: 0.000000\n",
+ "flat-->dense: 0.016507\n",
+ "-->dense: 0.927903\n",
+ "dense-->flat: 0.927903\n",
+ "flat-->pool: 0.302989\n",
+ "pool-->relu2: 0.000000\n",
+ "relu2-->conv2: 0.000000\n",
+ "conv2-->relu1: 0.000000\n",
+ "relu1-->conv1: 0.000000\n",
+ "\n",
+ " loss = 2.302492, params\n",
+ "conv1_weight 92.8762283325\n",
+ "conv1_bias 287.317565918\n",
+ "conv2_weight 23.6872882843\n",
+ "conv2_bias 2.9135928154\n",
+ "dense_weight 35.6038131714\n",
+ "dense_bias 0.0192515775561\n",
+ "\n",
+ "\n",
+ "Epoch 8\n",
+ "-->conv1: 394.178162\n",
+ "conv1-->relu1: 0.000000\n",
+ "relu1-->conv2: 2.913593\n",
+ "conv2-->relu2: 0.000000\n",
+ "relu2-->pool: 0.000000\n",
+ "pool-->flat: 0.000000\n",
+ "flat-->dense: 0.019252\n",
+ "-->dense: 1.218609\n",
+ "dense-->flat: 1.218609\n",
+ "flat-->pool: 0.397913\n",
+ "pool-->relu2: 0.000000\n",
+ "relu2-->conv2: 0.000000\n",
+ "conv2-->relu1: 0.000000\n",
+ "relu1-->conv1: 0.000000\n",
+ "\n",
+ " loss = 2.300582, params\n",
+ "conv1_weight 102.090713501\n",
+ "conv1_bias 320.808746338\n",
+ "conv2_weight 25.5548191071\n",
+ "conv2_bias 3.29988527298\n",
+ "dense_weight 39.0138893127\n",
+ "dense_bias 0.0218270029873\n",
+ "\n",
+ "\n",
+ "Epoch 9\n",
+ "-->conv1: 430.156555\n",
+ "conv1-->relu1: 0.000000\n",
+ "relu1-->conv2: 3.299885\n",
+ "conv2-->relu2: 0.000000\n",
+ "relu2-->pool: 0.000000\n",
+ "pool-->flat: 0.000000\n",
+ "flat-->dense: 0.021827\n",
+ "-->dense: 1.221773\n",
+ "dense-->flat: 1.221773\n",
+ "flat-->pool: 0.398946\n",
+ "pool-->relu2: 0.000000\n",
+ "relu2-->conv2: 0.000000\n",
+ "conv2-->relu1: 0.000000\n",
+ "relu1-->conv1: 0.000000\n",
+ "\n",
+ " loss = 2.302244, params\n",
+ "conv1_weight 110.383636475\n",
+ "conv1_bias 350.950500488\n",
+ "conv2_weight 27.2405776978\n",
+ "conv2_bias 3.64754581451\n",
+ "dense_weight 42.0904846191\n",
+ "dense_bias 0.0241855494678\n"
+ ]
+ }
+ ],
+ "source": [
+ "np.random.shuffle(idx)\n",
+ "ffnet.verbose=True\n",
+ "for pname, pval in zip(net.param_names(), net.param_values()):\n",
+ " if len(pval.shape) > 1:\n",
+ " pval.gaussian(0, 10)\n",
+ " else:\n",
+ " pval.set_value(0)\n",
+ " print pname, pval.shape, pval.l1()\n",
+ "for b in range(10):\n",
+ " print \"\\n\\nEpoch %d\" % b\n",
+ " x = train_x[idx[b * batch_size: (b + 1) * batch_size]]\n",
+ " y = train_y[idx[b * batch_size: (b + 1) * batch_size]]\n",
+ " tx.copy_from_numpy(x)\n",
+ " ty.copy_from_numpy(y)\n",
+ " grads, (l, a) = net.train(tx, ty)\n",
+ " print '\\n loss = %f, params' % l\n",
+ " for (s, p, g) in zip(net.param_names(), net.param_values(), grads):\n",
+ " opt.apply_with_lr(epoch, 0.01, g, p, str(s), b)\n",
+ " print s, p.l1()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def vis_square(data):\n",
+ " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
+ " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
+ " \n",
+ " # normalize data for display\n",
+ " data = (data - data.min()) / (data.max() - data.min())\n",
+ " \n",
+ " # force the number of filters to be square\n",
+ " n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+ " padding = (((0, n ** 2 - data.shape[0]),\n",
+ " (0, 1), (0, 1)) # add some space between filters\n",
+ " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n",
+ " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n",
+ " \n",
+ " # tile the filters into an image\n",
+ " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+ " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+ " \n",
+ " plt.imshow(data); plt.axis('off')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "NOTE: If your model was saved using pickle, then set use_pickle=True for loading it\n",
+ "conv2_bias\n",
+ "conv2_weight\n",
+ "dense_weight\n",
+ "conv1_bias\n",
+ "dense_bias\n",
+ "conv1_weight\n"
+ ]
+ }
+ ],
+ "source": [
+ "np.random.shuffle(idx)\n",
+ "ffnet.verbose=False\n",
+ "net.load('checkpoint')\n",
+ "b=1\n",
+ "x = train_x[idx[b * batch_size: (b + 1) * batch_size]] \n",
+ "tx.copy_from_numpy(x)\n",
+ "\n",
+ "r = net.forward(False, tx, ['relu1', 'relu2'])\n",
+ "\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f0747eafc90>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "r1 = tensor.to_numpy(r['relu1'])[0]\n",
+ "vis_square(r1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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gvDHzA0oRfkApwg8oVZFr/iBu\nNtQNRRescOp0Is8FSo2ZH1CK8ANKEX5AKcIPKFWRG36ZqY2iruG5eL7qiyId8EJP0BgLx0/YGA4Q\nCjM/oBThB5Qi/IBSKc/zrHXm9uXsdQYolW7qDPXGGzM/oBThB5Qi/IBShB9QyuqGH4DywcwPKEX4\nAaUIP6AU4QeUIvyAUoQfUIrwA0oRfkApwg8oRfgBpQg/oBThB5Qi/IBShB9QivADShF+QCnCDyhF\n+AGlCD+gFOEHlCL8gFKEH1CK8ANK/Q/zQX1soXEu2gAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f07356cc790>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "r2 = tensor.to_numpy(r['relu2'])[0]\n",
+ "vis_square(r2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(32, 288)\n"
+ ]
+ }
+ ],
+ "source": [
+ "p=net.param_values()[2]\n",
+ "print p.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMEX5AVOUHzBF+QFTqd7kU/PHu2Qme36fzuwrkZlPl
66Tmdo/rJCZ3HD9+qySCj1zCCHsuuJ5mdna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\nB0xRfsAU5QdMUX7AFOUHTKW6
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8qOfJntM7LqWHo/ZgQvOP9EyT31omM9fW6tt+Wn4zQ2b+/cjjMhOLkx8wRfkBU5QfMEX5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
AAAAAElF\nTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f07355967d0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "vis_square(tensor.to_numpy(p)[0].reshape(32, 3,3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:conda]",
+ "language": "python",
+ "name": "conda-env-conda-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.13"
+ },
+ "widgets": {
+ "state": {
+ "0678ea185e8c48a9ab20bdb956e6b18a": {
+ "views": [
+ {
+ "cell_index": 13
+ }
+ ]
+ },
+ "1373fb7b9e754b639a3f27ebf0372d70": {
+ "views": [
+ {
+ "cell_index": 13
+ }
+ ]
+ },
+ "49561f2ab00b457f82357766d967ca8b": {
+ "views": [
+ {
+ "cell_index": 13
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/core.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/core.ipynb b/doc/en/docs/notebook/core.ipynb
new file mode 100644
index 0000000..c16cc1f
--- /dev/null
+++ b/doc/en/docs/notebook/core.ipynb
@@ -0,0 +1,1151 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "# SINGA Core Classes\n",
+ "\n",
+ "<img src=\"http://singa.apache.org/en/_static/images/singav1-sw.png\" width=\"500px\"/>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "# Device\n",
+ "\n",
+ "A device instance represents a hardware device with multiple execution units, e.g.,\n",
+ "* A GPU which has multile cuda streams\n",
+ "* A CPU which has multiple threads\n",
+ "\n",
+ "All data structures (variables) are allocated on a device instance. Consequently, all operations are executed on the resident device.\n",
+ "\n",
+ "## Create a device instance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.singa_wrap.Device; proxy of <Swig Object of type 'std::shared_ptr< singa::Device > *' at 0x7f69a05ff330> >"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from singa import device\n",
+ "default_dev = device.get_default_device()\n",
+ "gpu = device.create_cuda_gpu() # the first gpu device\n",
+ "gpu"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "** NOTE: currently we can only call the creating function once due to the cnmem restriction.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "gpu = device.create_cuda_gpu_on(1) # use the gpu device with the specified GPU ID\n",
+ "gpu_list1 = device.create_cuda_gpus(2) # the first two gpu devices\n",
+ "gpu_list2 = device.create_cuda_gpus([0,2]) # create the gpu instances on the given GPU IDs\n",
+ "opencl_gpu = device.create_opencl_device() # valid if SINGA is compiled with USE_OPENCL=ON"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device.get_num_gpus()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0, 1, 2)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device.get_gpu_ids()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tensor\n",
+ "\n",
+ "A tensor instance represents a multi-dimensional array allocated on a device instance.\n",
+ "It provides linear algbra operations, like +, -, *, /, dot, pow ,etc\n",
+ "\n",
+ "NOTE: class memeber functions are inplace; global functions are out-of-place."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create tensor instances"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2, 3)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from singa import tensor\n",
+ "import numpy as np\n",
+ "a = tensor.Tensor((2, 3))\n",
+ "a.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.singa_wrap.Device; proxy of <Swig Object of type 'std::shared_ptr< singa::Device > *' at 0x7f69a02f8a50> >"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a.device"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "gb = tensor.Tensor((2, 3), gpu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.singa_wrap.Device; proxy of <Swig Object of type 'std::shared_ptr< singa::Device > *' at 0x7f69a05ff330> >"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gb.device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Initialize tensor values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "a.set_value(1.2)\n",
+ "gb.gaussian(0, 0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### To and from numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.20000005, 1.20000005, 1.20000005],\n",
+ " [ 1.20000005, 1.20000005, 1.20000005]], dtype=float32)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.24042693, -0.21337385, -0.0969397 ],\n",
+ " [-0.010797 , -0.07642138, -0.09220808]], dtype=float32)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.to_numpy(gb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2,)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c = tensor.from_numpy(np.array([1,2], dtype=np.float32))\n",
+ "c.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 3., 4.], dtype=float32)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c.copy_from_numpy(np.array([3,4], dtype=np.float32))\n",
+ "tensor.to_numpy(c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Move tensor between devices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.singa_wrap.Device; proxy of <Swig Object of type 'std::shared_ptr< singa::Device > *' at 0x7f69a05ff330> >"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gc = c.clone()\n",
+ "gc.to_device(gpu)\n",
+ "gc.device"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<singa.singa_wrap.Device; proxy of <Swig Object of type 'std::shared_ptr< singa::Device > *' at 0x7f69a02f8a50> >"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "b = gb.clone()\n",
+ "b.to_host() # the same as b.to_device(default_dev)\n",
+ "b.device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Operations\n",
+ "\n",
+ "**NOTE: tensors should be initialized if the operation would read the tensor values**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.12169448286294937"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gb.l1()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4898979663848877"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a.l2()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "e = tensor.Tensor((2, 3))\n",
+ "e.is_empty()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6L"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gb.size()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "24L"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gb.memsize()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# note we can only support matrix multiplication for tranposed tensors; \n",
+ "# other operations on transposed tensor would result in errors\n",
+ "c.is_transpose() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "et=e.T()\n",
+ "et.is_transpose()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3L, 2L)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "et.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2L"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "et.ndim()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Member functions (in-place)\n",
+ "\n",
+ "These functions would change the content of the tensor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.44042695, 0.98662621, 1.10306036],\n",
+ " [ 1.18920302, 1.12357867, 1.10779202]], dtype=float32)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a += b\n",
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.20000005, 1.20000005, 1.20000005],\n",
+ " [ 1.20000005, 1.20000005, 1.20000005]], dtype=float32)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a -= b\n",
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 2.4000001, 2.4000001, 2.4000001],\n",
+ " [ 2.4000001, 2.4000001, 2.4000001]], dtype=float32)"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a *= 2\n",
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.80000007, 0.80000007, 0.80000007],\n",
+ " [ 0.80000007, 0.80000007, 0.80000007]], dtype=float32)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a /= 3\n",
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.62944734, -0.72904599, 0.81158388], dtype=float32)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d = tensor.Tensor((3,))\n",
+ "d.uniform(-1,1)\n",
+ "tensor.to_numpy(d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.42944741, 0.07095408, 1.61158395],\n",
+ " [ 1.42944741, 0.07095408, 1.61158395]], dtype=float32)"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a.add_row(d)\n",
+ "tensor.to_numpy(a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Global functions (out of place)\n",
+ "\n",
+ "These functions would not change the memory of the tensor, instead they return a new tensor\n",
+ "\n",
+ "**Unary functions**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1., -1., 1.], dtype=float32)"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "h = tensor.sign(d)\n",
+ "tensor.to_numpy(h)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.62944734, -0.72904599, 0.81158388], dtype=float32)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.to_numpy(d)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.62944734, 0.72904599, 0.81158388], dtype=float32)"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "h = tensor.abs(d)\n",
+ "tensor.to_numpy(h)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.62944734, 0. , 0.81158388], dtype=float32)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "h = tensor.relu(d)\n",
+ "tensor.to_numpy(h)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3L,)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "g = tensor.sum(a, 0)\n",
+ "g.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2L,)"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "g = tensor.sum(a, 1)\n",
+ "g.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1., 0.], dtype=float32)"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.bernoulli(0.5, g)\n",
+ "tensor.to_numpy(g)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.12226005, -0.05827543], dtype=float32)"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "g.gaussian(0, 0.2)\n",
+ "tensor.gaussian(0, 0.2, g)\n",
+ "tensor.to_numpy(g)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Binary functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.66987431, -0.14241977, 1.51464427],\n",
+ " [ 1.41865039, -0.0054673 , 1.51937592]], dtype=float32)"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = a + b\n",
+ "tensor.to_numpy(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0., 0., 0.],\n",
+ " [ 0., 0., 0.]], dtype=float32)"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "g = a < b\n",
+ "tensor.to_numpy(g)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 7.66987419, 5.85758018, 7.51464415],\n",
+ " [ 9.41865063, 7.99453259, 9.5193758 ]], dtype=float32)"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.add_column(2, c, 1, f) # f = 2 *c + 1* f\n",
+ "tensor.to_numpy(f)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### BLAS\n",
+ "\n",
+ "BLAS function may change the memory of input tensor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.24042693, -0.21337385, -0.0969397 ],\n",
+ " [-0.010797 , -0.07642138, -0.09220808]], dtype=float32)"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.axpy(2, a, f) # f = 2a + f\n",
+ "tensor.to_numpy(b)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.17231143, -0.16945721],\n",
+ " [ 0.17231143, -0.16945721]], dtype=float32)"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = tensor.mult(a, b.T())\n",
+ "tensor.to_numpy(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.51693428, -0.50837165],\n",
+ " [ 0.51693428, -0.50837165]], dtype=float32)"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor.mult(a, b.T(), f, 2, 1) # f = 2a*b.T() + 1f\n",
+ "tensor.to_numpy(f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "## Next: [SINGA model classes](./model.ipynb)"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [conda env:pysinga]",
+ "language": "python",
+ "name": "conda-env-pysinga-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
[3/8] incubator-singa git commit: SINGA-305 - Add jupyter notebooks
for SINGA V1 tutorial
Posted by ka...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/5144bcf1/doc/en/docs/notebook/rbm.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/rbm.ipynb b/doc/en/docs/notebook/rbm.ipynb
index c365eb1..c9aadf5 100755
--- a/doc/en/docs/notebook/rbm.ipynb
+++ b/doc/en/docs/notebook/rbm.ipynb
@@ -18,7 +18,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 6,
"metadata": {
"collapsed": false
},
@@ -30,8 +30,8 @@
"import argparse\n",
"import cPickle\n",
"import utils\n",
- "import Image\n",
"import urllib\n",
+ "from PIL import Image\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
@@ -59,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 7,
"metadata": {
"collapsed": true
},
@@ -68,8 +68,7 @@
"from singa import initializer\n",
"from singa import optimizer\n",
"from singa import device\n",
- "from singa import tensor\n",
- "from singa.proto import core_pb2"
+ "from singa import tensor\n"
]
},
{
@@ -86,7 +85,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {
"collapsed": false
},
@@ -141,7 +140,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 9,
"metadata": {
"collapsed": false
},
@@ -150,23 +149,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch 0, Reconstruction error per image = 17.937706\n",
- "Epoch 1, Reconstruction error per image = 10.943875\n",
- "Epoch 2, Reconstruction error per image = 9.583109\n",
- "Epoch 3, Reconstruction error per image = 8.912214\n",
- "Epoch 4, Reconstruction error per image = 8.491555\n",
- "Epoch 5, Reconstruction error per image = 8.196870\n",
- "Epoch 6, Reconstruction error per image = 7.985176\n",
- "Epoch 7, Reconstruction error per image = 7.813920\n",
- "Epoch 8, Reconstruction error per image = 7.697145\n",
- "Epoch 9, Reconstruction error per image = 7.588426\n"
+ "Epoch 0, Reconstruction error per image = 17.989441\n",
+ "Epoch 1, Reconstruction error per image = 10.955967\n",
+ "Epoch 2, Reconstruction error per image = 9.593508\n",
+ "Epoch 3, Reconstruction error per image = 8.900522\n",
+ "Epoch 4, Reconstruction error per image = 8.485637\n",
+ "Epoch 5, Reconstruction error per image = 8.187351\n",
+ "Epoch 6, Reconstruction error per image = 7.981121\n",
+ "Epoch 7, Reconstruction error per image = 7.825888\n",
+ "Epoch 8, Reconstruction error per image = 7.707948\n",
+ "Epoch 9, Reconstruction error per image = 7.589963\n"
]
},
{
"data": {
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<TRUNCATED>