The following is adapted from Visualizing TensorFlow Graphs in Jupyter Notebooks
And excuted in
docker run -it -p 8888:8888 -p 6006:6006 -v `pwd`:/space/ -w /space/ --rm --name md waleedka/modern-deep-learning jupyter notebook --ip=0.0.0.0 --allow-root
In [1]:
import tensorflow as tf
g = tf.Graph()
with g.as_default():
a = tf.placeholder(tf.float32, name="a")
b = tf.placeholder(tf.float32, name="b")
c = a + b
In [2]:
[node.name for node in g.as_graph_def().node]
Out[2]:
['a', 'b', 'add']
In [3]:
g.as_graph_def().node[2].input
Out[3]:
['a', 'b']
In [14]:
%%bash
export DEBIAN_FRONTEND=noninteractive
apt-get update
apt-get install -yq --no-install-recommends graphviz
Hit:1 http://archive.ubuntu.com/ubuntu xenial InRelease
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Get:4 http://archive.ubuntu.com/ubuntu xenial-backports InRelease [102 kB]
Fetched 306 kB in 2s (123 kB/s)
Reading package lists...
Reading package lists...
Building dependency tree...
Reading state information...
graphviz is already the newest version (2.38.0-12ubuntu2.1).
0 upgraded, 0 newly installed, 0 to remove and 73 not upgraded.
In [6]:
%%bash
pip install graphviz
Collecting graphviz
Downloading graphviz-0.8.1-py2.py3-none-any.whl
Installing collected packages: graphviz
Successfully installed graphviz-0.8.1
In [15]:
from graphviz import Digraph
dot = Digraph()
for n in g.as_graph_def().node:
# Each node has a name and a label. The name identifies the node
# while the label is what will be displayed in the graph.
# We're using the name as a label for simplicity.
dot.node(n.name, label=n.name)
for i in n.input:
# Edges are determined by the names of the nodes
dot.edge(i, n.name)
# Jupyter can automatically display the DOT graph,
# which allows us to just return it as a value.
dot
Out[15]:
In [16]:
def tf_to_dot(graph):
dot = Digraph()
for n in g.as_graph_def().node:
dot.node(n.name, label=n.name)
for i in n.input:
dot.edge(i, n.name)
return dot
In [17]:
g = tf.Graph()
with g.as_default():
pi = tf.constant(3.14, name="pi")
r = tf.placeholder(tf.float32, name="r")
y = pi * r * r
tf_to_dot(g)
Out[17]:
In [24]:
%%bash
mkdir vis_logs
Run the follwing:
docker exec -it md tensorboard --logdir=dl/vis_logs
And navigate to http://localhost:6006/#graphs
In [26]:
g = tf.Graph()
with g.as_default():
pi = tf.constant(3.14, name="pi")
r = tf.placeholder(tf.float32, name="r")
y = pi * r * r
tf.summary.FileWriter("vis_logs", g).close()
In [27]:
g = tf.Graph()
with g.as_default():
X = tf.placeholder(tf.float32, name="X")
W1 = tf.placeholder(tf.float32, name="W1")
b1 = tf.placeholder(tf.float32, name="b1")
a1 = tf.nn.relu(tf.matmul(X, W1) + b1)
W2 = tf.placeholder(tf.float32, name="W2")
b2 = tf.placeholder(tf.float32, name="b2")
a2 = tf.nn.relu(tf.matmul(a1, W2) + b2)
W3 = tf.placeholder(tf.float32, name="W3")
b3 = tf.placeholder(tf.float32, name="b3")
y_hat = tf.matmul(a2, W3) + b3
tf.summary.FileWriter("vis_logs", g).close()
In [30]:
g = tf.Graph()
with g.as_default():
X = tf.placeholder(tf.float32, name="X")
with tf.name_scope("Layer1"):
W1 = tf.placeholder(tf.float32, name="W1")
b1 = tf.placeholder(tf.float32, name="b1")
a1 = tf.nn.relu(tf.matmul(X, W1) + b1)
with tf.name_scope("Layer2"):
W2 = tf.placeholder(tf.float32, name="W2")
b2 = tf.placeholder(tf.float32, name="b2")
a2 = tf.nn.relu(tf.matmul(a1, W2) + b2)
with tf.name_scope("Layer3"):
W3 = tf.placeholder(tf.float32, name="W3")
b3 = tf.placeholder(tf.float32, name="b3")
y_hat = tf.matmul(a2, W3) + b3
tf.summary.FileWriter("vis_logs", g).close()
In [33]:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
# TensorFlow Graph visualizer code
import numpy as np
from IPython.display import clear_output, Image, display, HTML
def strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def."""
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom(n0)
if n.op == 'Const':
tensor = n.attr['value'].tensor
size = len(tensor.tensor_content)
if size > max_const_size:
tensor.tensor_content = "<stripped %d bytes>"%size
return strip_def
def show_graph(graph_def, max_const_size=32):
"""Visualize TensorFlow graph."""
if hasattr(graph_def, 'as_graph_def'):
graph_def = graph_def.as_graph_def()
strip_def = strip_consts(graph_def, max_const_size=max_const_size)
code = """
<script src="//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js"></script>
<script>
function load() {{
document.getElementById("{id}").pbtxt = {data};
}}
</script>
<link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
<div style="height:600px">
<tf-graph-basic id="{id}"></tf-graph-basic>
</div>
""".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))
iframe = """
<iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>
""".format(code.replace('"', '"'))
display(HTML(iframe))
In [32]:
# Simply call this to display the result. Unfortunately it doesn't save the output together with
# the Jupyter notebook, so we can only show a non-interactive image here.
show_graph(g)
The following is adapted from Visualizing CNN architectures side by side with mxnet
In [36]:
%%bash
pip install mxnet
Collecting mxnet
Downloading mxnet-0.12.1-py2.py3-none-manylinux1_x86_64.whl (25.7MB)
Requirement already satisfied: numpy in /usr/local/lib/python3.5/dist-packages (from mxnet)
Requirement already satisfied: graphviz in /usr/local/lib/python3.5/dist-packages (from mxnet)
Requirement already satisfied: requests in /usr/local/lib/python3.5/dist-packages (from mxnet)
Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.5/dist-packages (from requests->mxnet)
Installing collected packages: mxnet
Successfully installed mxnet-0.12.1
In [48]:
%%bash
# https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-vgg.md
wget http://data.dmlc.ml/mxnet/models/imagenet/vgg/vgg19.tar.gz
--2017-11-24 00:26:22-- http://data.dmlc.ml/mxnet/models/imagenet/vgg/vgg19.tar.gz
Resolving data.dmlc.ml (data.dmlc.ml)... 54.208.175.7
Connecting to data.dmlc.ml (data.dmlc.ml)|54.208.175.7|:80... connected.
HTTP request sent, awaiting response... 404 Not Found
2017-11-24 00:26:25 ERROR 404: Not Found.
In [45]:
%%bash
wget http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json
--2017-11-23 01:55:04-- http://data.dmlc.ml/models/imagenet/inception-bn/Inception-BN-symbol.json
Resolving data.dmlc.ml (data.dmlc.ml)... 54.208.175.7
Connecting to data.dmlc.ml (data.dmlc.ml)|54.208.175.7|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 116922 (114K) [application/json]
Saving to: 'Inception-BN-symbol.json'
0K .......... .......... .......... .......... .......... 43% 23.1K 3s
50K .......... .......... .......... .......... .......... 87% 16.4K 1s
100K .......... .... 100% 10.2K=6.6s
2017-11-23 01:55:13 (17.3 KB/s) - 'Inception-BN-symbol.json' saved [116922/116922]
In [46]:
%%bash
cat Inception-BN-symbol.json
{
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{
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{
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{
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{
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{
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{
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{
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In [49]:
%%bash
wget http://data.dmlc.ml/mxnet/models/imagenet/resnet/50-layers/resnet-50-symbol.json && wget http://data.dmlc.ml/mxnet/models/imagenet/resnet/50-layers/resnet-50-0000.params
Process is interrupted.
In [62]:
import mxnet as mx
sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)
mx.viz.plot_network(sym, node_attrs={"shape":'rect',"fixedsize":'false'}, save_format='png')
Out[62]:
In [55]:
import mxnet as mx
user = mx.symbol.Variable('user')
item = mx.symbol.Variable('item')
score = mx.symbol.Variable('score')
# Set dummy dimensions
k = 64
max_user = 100
max_item = 50
# user feature lookup
user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
# item feature lookup
item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
# predict by the inner product, which is elementwise product and then sum
net = user * item
net = mx.symbol.sum_axis(data = net, axis = 1)
net = mx.symbol.Flatten(data = net)
# loss layer
net = mx.symbol.LinearRegressionOutput(data = net, label = score)
# Visualize your network
mx.viz.plot_network(net)
Out[55]:
In [ ]:
Content source: utensil/julia-playground
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