http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/bfeb6127/doc/en/docs/notebook/model.ipynb
----------------------------------------------------------------------
diff --git a/doc/en/docs/notebook/model.ipynb b/doc/en/docs/notebook/model.ipynb
index 23a5553..6888435 100644
--- a/doc/en/docs/notebook/model.ipynb
+++ b/doc/en/docs/notebook/model.ipynb
@@ -29,7 +29,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -50,7 +50,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -67,9 +67,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -83,14 +81,14 @@
"dense = Dense('dense', 3, input_sample_shape=(2,))\n",
"#dense.param_names()\n",
"w, b = dense.param_values()\n",
- "print w.shape, b.shape"
+ "print(w.shape, b.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
- "collapsed": false
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -101,15 +99,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.02440065, -0.03396009, 0.01396658],\n",
- " [ 0.00771775, 0.07841966, -0.05931653]], dtype=float32)"
+ " [ 0.00771775, 0.07841966, -0.05931654]], dtype=float32)"
]
},
"execution_count": 5,
@@ -127,9 +123,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -141,7 +135,7 @@
],
"source": [
"gx, [gw, gb] = dense.backward(True, y)\n",
- "print gx.shape, gw.shape, gb.shape"
+ "print(gx.shape, gw.shape, gb.shape)"
]
},
{
@@ -154,9 +148,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -168,7 +160,7 @@
],
"source": [
"conv = Conv2D('conv', 4, 3, 1, input_sample_shape=(3, 6, 6))\n",
- "print conv.get_output_sample_shape()"
+ "print(conv.get_output_sample_shape())"
]
},
{
@@ -181,9 +173,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -195,7 +185,7 @@
],
"source": [
"pool = MaxPooling2D('pool', 3, 2, input_sample_shape=(4, 6, 6))\n",
- "print pool.get_output_sample_shape()"
+ "print(pool.get_output_sample_shape())"
]
},
{
@@ -219,9 +209,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -233,15 +221,13 @@
],
"source": [
"split = Split('split', 2, input_sample_shape=(4, 6, 6))\n",
- "print split.get_output_sample_shape()"
+ "print(split.get_output_sample_shape())"
]
},
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -253,15 +239,13 @@
],
"source": [
"merge = Merge('merge', input_sample_shape=(4, 6, 6))\n",
- "print merge.get_output_sample_shape()"
+ "print(merge.get_output_sample_shape())"
]
},
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -273,15 +257,13 @@
],
"source": [
"sli = Slice('slice', 1, [2], input_sample_shape=(4, 6, 6))\n",
- "print sli.get_output_sample_shape()"
+ "print(sli.get_output_sample_shape())"
]
},
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -293,7 +275,7 @@
],
"source": [
"concat = Concat('concat', 1, input_sample_shapes=[(3, 6, 6), (1, 6,
6)])\n",
- "print concat.get_output_sample_shape()"
+ "print(concat.get_output_sample_shape())"
]
},
{
@@ -306,9 +288,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -328,26 +308,24 @@
"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",
+ "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"
+ "print(acc)"
]
},
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1.80309379101\n",
+ "1.8030937910079956\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"
@@ -364,8 +342,8 @@
"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)"
+ "print(l.l1())\n",
+ "print(tensor.to_numpy(g))"
]
},
{
@@ -378,14 +356,12 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<singa.tensor.Tensor at 0x7f6a0c7cfe90>"
+ "<singa.tensor.Tensor at 0x7f539260f710>"
]
},
"execution_count": 16,
@@ -416,20 +392,18 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"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"
+ "('conv1', (32, 32, 32))\n",
+ "('relu1', (32, 32, 32))\n",
+ "('pool1', (32, 16, 16))\n",
+ "('flat', (8192,))\n",
+ "('dense', (10,))\n",
+ "['conv1/weight', 'conv1/bias', 'dense/weight', 'dense/bias']\n"
]
}
],
@@ -449,25 +423,23 @@
" p.set_value(0)\n",
" else:\n",
" p.gaussian(0, 0.01)\n",
- "print net.param_names()"
+ "print(net.param_names())"
]
},
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"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"
+ "('conv1', (32, 32, 32))\n",
+ "('relu1', (32, 32, 32))\n",
+ "('pool1', (32, 16, 16))\n",
+ "('flat', (8192,))\n",
+ "('dense', (10,))\n"
]
}
],
@@ -514,21 +486,21 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [conda env:conda]",
+ "display_name": "py3",
"language": "python",
- "name": "conda-env-conda-py"
+ "name": "py3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.13"
+ "pygments_lexer": "ipython3",
+ "version": "3.5.3"
}
},
"nbformat": 4,
