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mnist-visualizations.py
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mnist-visualizations.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: mnist-visualizations.py
import os
import argparse
"""
MNIST ConvNet example with weights/activations visualization.
"""
from tensorpack import *
from tensorpack.dataflow import dataset
import tensorflow as tf
IMAGE_SIZE = 28
def visualize_conv_weights(filters, name):
"""Visualize use weights in convolution filters.
Args:
filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
image of all weight
"""
with tf.name_scope('visualize_w_' + name):
filters = tf.transpose(filters, (3, 2, 0, 1)) # [h, w, cin, cout] -> [cout, cin, h, w]
filters = tf.unstack(filters) # --> cout * [cin, h, w]
filters = tf.concat(filters, 1) # --> [cin, cout * h, w]
filters = tf.unstack(filters) # --> cin * [cout * h, w]
filters = tf.concat(filters, 1) # --> [cout * h, cin * w]
filters = tf.expand_dims(filters, 0)
filters = tf.expand_dims(filters, -1)
tf.summary.image('visualize_w_' + name, filters)
def visualize_conv_activations(activation, name):
"""Visualize activations for convolution layers.
Remarks:
This tries to place all activations into a square.
Args:
activation: tensor with the activation [B,H,W,C]
name: label for tensorboard
Returns:
image of almost all activations
"""
import math
with tf.name_scope('visualize_act_' + name):
_, h, w, c = activation.get_shape().as_list()
rows = []
c_per_row = int(math.sqrt(c))
for y in range(0, c - c_per_row, c_per_row):
row = activation[:, :, :, y:y + c_per_row] # [?, H, W, 32] --> [?, H, W, 5]
cols = tf.unstack(row, axis=3) # [?, H, W, 5] --> 5 * [?, H, W]
row = tf.concat(cols, 1)
rows.append(row)
viz = tf.concat(rows, 2)
tf.summary.image('visualize_act_' + name, tf.expand_dims(viz, -1))
class Model(ModelDesc):
def _get_inputs(self):
return [InputDesc(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
InputDesc(tf.int32, (None,), 'label')]
def _build_graph(self, inputs):
image, label = inputs
image = tf.expand_dims(image * 2 - 1, 3)
with argscope(Conv2D, kernel_shape=3, nl=tf.nn.relu, out_channel=32):
c0 = Conv2D('conv0', image)
p0 = MaxPooling('pool0', c0, 2)
c1 = Conv2D('conv1', p0)
c2 = Conv2D('conv2', c1)
p1 = MaxPooling('pool1', c2, 2)
c3 = Conv2D('conv3', p1)
fc1 = FullyConnected('fc0', c3, 512, nl=tf.nn.relu)
fc1 = Dropout('dropout', fc1, 0.5)
logits = FullyConnected('fc1', fc1, out_dim=10, nl=tf.identity)
with tf.name_scope('visualizations'):
visualize_conv_weights(c0.variables.W, 'conv0')
visualize_conv_activations(c0, 'conv0')
visualize_conv_weights(c1.variables.W, 'conv1')
visualize_conv_activations(c1, 'conv1')
visualize_conv_weights(c2.variables.W, 'conv2')
visualize_conv_activations(c2, 'conv2')
visualize_conv_weights(c3.variables.W, 'conv3')
visualize_conv_activations(c3, 'conv3')
tf.summary.image('input', (image + 1.0) * 128., 3)
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
accuracy = tf.reduce_mean(tf.to_float(tf.nn.in_top_k(logits, label, 1)), name='accuracy')
wd_cost = tf.multiply(1e-5,
regularize_cost('fc.*/W', tf.nn.l2_loss),
name='regularize_loss')
self.cost = tf.add_n([wd_cost, cost], name='total_cost')
summary.add_moving_summary(cost, wd_cost, self.cost, accuracy)
summary.add_param_summary(('.*/W', ['histogram', 'rms']))
def _get_optimizer(self):
lr = tf.train.exponential_decay(
learning_rate=1e-3,
global_step=get_global_step_var(),
decay_steps=468 * 10,
decay_rate=0.3, staircase=True, name='learning_rate')
tf.summary.scalar('lr', lr)
return tf.train.AdamOptimizer(lr)
def get_data():
train = BatchData(dataset.Mnist('train'), 128)
test = BatchData(dataset.Mnist('test'), 256, remainder=True)
return train, test
def get_config():
logger.auto_set_dir()
dataset_train, dataset_test = get_data()
return TrainConfig(
model=Model(),
dataflow=dataset_train,
callbacks=[
ModelSaver(),
InferenceRunner(
dataset_test, ScalarStats(['cross_entropy_loss', 'accuracy'])),
],
steps_per_epoch=dataset_train.size(),
max_epoch=100,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
launch_train_with_config(config, SimpleTrainer())