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# Copyright 2017 Google, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#scalars tab is for scalar summaries
#accuracy and cost function
#x axis shows time steps
#y axis shows accuracy or loss
#increase graph for closer look or view wider range of data points (expands y axis) with these two buttons
#can also draw rectange to zoom in on region
#double click to zoom out
#as we mouse over chart, it will produce crosshairs with data values
#we can adjust smoothness of line with slider
#step option shows timesteps
#relative option shows time relative to first data point (num minutes or hours since training run was started)
#wall shows actual times runs happened
#we can create new tags in side bar, entirely new or can group existing summaries together
#images tab
#image summary
#still haven't seen audio example in TF
#magenta is a good example
#graph tab
#name scopes as squares
#direction of arrows shows the direction tensors are flowing
#placeholder x shows entrypoint for data
#to reduce clutter TB shows node connected to many others nodes in there own section in detail
#we could add and remove to main graph if we wanted to
#we could see the values at each time step if we wanted to
#we can see either which structure are being used or which devices each oeration is running on
#we could manually upload a saved TF graph right from the UI if we'd like
#the graph detects structures that are the exact same and colors them the same , unless its grey thats just default
#we can see how many tensors inside the lines
#more name scopes, the more abstraction, simpler the graph
#distributions
#to visualize the distributions of activations coming off a particular layer, or the distribution of gradients or weights.
#histogram
#The histogram plot allows you to plot variables from your graph.
#So if you're model has weights, the histogram shows how the values of those weights change with training.
#embeddings
#PCA vs T-SNE
#The idea of SNE and t-SNE is to place neighbors close to each other, (almost) completly ignoring the global structure.
#PCA is quite the opposite. It tries to preserve the global properties (eigenvectors with high variance) while it may lose low-variance deviations between neighbors.
#http://stats.stackexchange.com/questions/238538/are-there-cases-where-pca-is-more-suitable-than-t-sne/249520#249520
#can also load training weights
#find neighbors
#share finding
#need moar plugins
#You can also construct custom specialized linear projections based on text searches for finding meaningful directions in space.
import os
import tensorflow as tf
import sys
import urllib
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#versioning, urllib named differently for dif python versions
if sys.version_info[0] >= 3:
from urllib.request import urlretrieve
else:
from urllib import urlretrieve
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#define our github URLs
LOGDIR = 'mnist_tutorial/'
GITHUB_URL ='https://raw.githubusercontent.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial/master/'
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### MNIST EMBEDDINGS ###
#downloads and reads the data
#The MNIST data is split into three parts: 55,000 data points of training data (mnist.train), 10,000 points of test data (mnist.test), and 5,000 points of validation data (mnist.validation).
#very MNIST data point has two parts: an image of a handwritten digit and a corresponding label. We'll call the images "x" and the labels "y".
#Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers:
#https://www.tensorflow.org/images/MNIST-Matrix.png
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
#If you have images associated with your embeddings, you will need to produce a single image consisting of
# small thumbnails of each data point. This is known as the sprite image. The sprite should have the same number
#of rows and columns with thumbnails stored in row-first order: the first data point placed in the
#top left and the last data point in the bottom right:
#TSV is a file extension for a tab-delimited file used with spreadsheet software.
#TSV stands for Tab Separated Values. TSV files are used for raw data and can be
#imported into and exported from spreadsheet software.
urlretrieve(GITHUB_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urlretrieve(GITHUB_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
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#first we'll define layers as re-usable functions
#Typically, a CNN is composed of a stack of convolutional modules that perform feature extraction.
#Each module consists of a convolutional layer followed by a pooling layer. The last convolutional module
#is followed by one or more dense layers that perform classification. The final dense layer in a CNN contains
#a single node for each target class in the model (all the possible classes the model may predict), with a
#softmax activation function to generate a value between 0–1 for each node (the sum of all these softmax values
#is equal to 1). We can interpret the softmax values for a given image as relative measurements of how likely
#it is that the image falls into each target class.
#Summary - tensor flow op that outputs protocol buffers
#Scalar summary - single value (line charts)
#Image summary (visualize images) generative
#Audio
#Histogram (see dsitbrutiuons of values ) weights
#Tensor (any kind) in development
# we can merge them all at the end
# Add convolution layer
def conv_layer(input, size_in, size_out, name="conv"):
#tf.name_scope creates namespace for operators in the default graph, places into group, easier to read
#A graph maintains a stack of name scopes. A `with name_scope(...):`
#statement pushes a new name onto the stack for the lifetime of the context.
#Ops have names, name scopes group ops
with tf.name_scope(name):
#A variable maintains state in the graph across calls to run(). You add a variable to the graph by constructing an instance of the class Variable.
#truncated normal Outputs random values from a truncated normal distribution.
w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
#constant Creates a constant tensor.
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
#Computes a 2-D convolution given 4-D input and filter tensors.
#1 Flattens the filter to a 2-D matrix
#2 Extracts image patches from the input tensor to form a virtual tensor
#3 For each patch, right-multiplies the filter matrix and the image patch vector.
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
#nonlin relu reduces likelihood of vanishing gradient, most used activation function these days
act = tf.nn.relu(conv + b)
# , or the distribution of gradients or weights.
#we can collect this data by attaching tf.summary.histogram ops to the gradient outputs and to the variable that holds weights, respectively.
#visualize the the distribution of weights and biases
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
##so we can visualize the distributions of activations coming off this layer
tf.summary.histogram("activations", act)
#Let's say we have an 4x4 matrix representing our initial input.
#Let's say as well that we have a 2x2 filter that we'll run over our input.
#We'll have a stride of 2 (meaning the (dx, dy) for stepping over our input will be (2, 2)) and won't overlap regions.
#For each of the regions represented by the filter, we will take the max of that region and create a new, output matrix
#where each element is the max of a region in the original input.
#https://qph.ec.quoracdn.net/main-qimg-8afedfb2f82f279781bfefa269bc6a90-p
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
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#Dense (fully connected) layers perform classification on the
#features extracted by the convolutional layers and downsampled by the
#pooling layers. In a dense layer, every node in the layer is connected to every node in the preceding layer.
# Add fully connected layer
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
#fully connected part
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
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#build our model
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
#for the data (images)
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
#Outputs a Summary protocol buffer with images.
tf.summary.image('input', x_image, 3)
#for the labels
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
#2 conv layers or 1?
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
#We can flatten this array into a vector of 28x28 = 784 numbers.
#It doesn't matter how we flatten the array, as long as we're consistent
#between images. From this perspective, the MNIST images are just a bunch of
#points in a 784-dimensional vector space, with a very rich structure
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
#2 fully connected layers or one?
if use_two_fc:
#give it the flattened image tensor
fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, "fc1")
#we want these embeeddings to visualize them later
embedding_input = fc1
embedding_size = 1024
logits = fc_layer(fc1, 1024, 10, "fc2")
else:
#else we take them directly from the conv layer
embedding_input = flattened
embedding_size = 7*7*64
#logits the sum of the inputs may not equal 1, that the values are not probabilities
#we'll feed these to the last (softmax) to make them probabilities
logits = fc_layer(flattened, 7*7*64, 10, "fc")
#short for cross entropy loss
with tf.name_scope("xent"):
#Computes the mean of elements across dimensions of a tensor.
#so in this case across output probabilties
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
#save that single number
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
#Adam offers several advantages over the simple tf.train.GradientDescentOptimizer.
#Foremost is that it uses moving averages of the parameters (momentum);
#This enables Adam to use a larger effective step size, and the algorithm will converge to this step size without fine tuning.
#The main down side of the algorithm is that Adam requires more computation to be performed for each parameter
#in each training step (to maintain the moving averages and variance, and calculate the scaled gradient);
#and more state to be retained for each parameter (approximately tripling the size of the model to store the average and variance for each parameter).
#A simple tf.train.GradientDescentOptimizer could equally be used in your MLP, but would require more hyperparameter tuning before it would converge as quickly.
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
#Returns the index with the largest value across axes of a tensor.
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
#Casts a tensor to a new type.
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
#merge them all so one write to disk, more comp efficient
summ = tf.summary.merge_all()
#intiialize embedding matrix as 0s
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
#give it calculated embedding
assignment = embedding.assign(embedding_input)
#initialize the saver
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
#filewriter is how we write the summary protocol buffers to disk
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
## Format: tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
## You can add multiple embeddings. Here we add only one.
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
#training step
for i in range(2001):
batch = mnist.train.next_batch(100)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
#save checkpoints
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
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def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv=2" if use_two_conv else "conv=1"
fc_param = "fc=2" if use_two_fc else "fc=1"
return "lr_%.0E,%s,%s" % (learning_rate, conv_param, fc_param)
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def main():
# You can try adding some more learning rates
for learning_rate in [1E-4]:
# Include "False" as a value to try different model architectures
for use_two_fc in [True]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3,fc=2,conv=2)
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
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# if __name__ == '__main__':
main()