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ex07_tf_guided_backprop.py
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ex07_tf_guided_backprop.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import time
import random
import math
import numpy as np
import h5py
import tensorflow as tf
import MLUtil as util
import TFModel
def train(train_files, validation_files, saved_model):
t0 = time.time()
numOutputs = 2
training_X, training_Y = util.readData(train_files, 'xtcavimg', 'lasing', 'tf', numOutputs)
validation_X, validation_Y = util.readData(validation_files, 'xtcavimg', 'lasing', 'tf', numOutputs)
read_time = time.time()-t0
minibatch_size = 24
batches_per_epoch = len(training_X)//minibatch_size
print("-- read %d samples in %.2fsec. batch_size=%d, %d batches per epoch" %
(len(training_X)+len(validation_X), read_time, minibatch_size, batches_per_epoch))
sys.stdout.flush()
VALIDATION_SIZE = 80
util.shuffle_data(validation_X, validation_Y)
validation_X = validation_X[0:VALIDATION_SIZE]
validation_Y = validation_Y[0:VALIDATION_SIZE]
img_placeholder = tf.placeholder(tf.int16,
shape=(None,363,284,1),
name='img')
labels_placeholder = tf.placeholder(tf.float32,
shape=(None, numOutputs),
name='labels')
train_placeholder = tf.placeholder(tf.bool, name='trainflag')
model = TFModel.build_model(img_placeholder, train_placeholder, numOutputs=2)
predict_op = tf.nn.softmax(model.final_logits)
## loss
cross_entropy_loss_all = tf.nn.softmax_cross_entropy_with_logits(model.final_logits,
labels_placeholder)
cross_entropy_loss = tf.reduce_mean(cross_entropy_loss_all)
loss = cross_entropy_loss
## training
global_step = tf.Variable(0, trainable=False)
lr = 0.002
learning_rate = tf.train.exponential_decay(learning_rate=lr,
global_step=global_step,
decay_steps=100,
decay_rate=0.96,
staircase=True)
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
train_op = optimizer.minimize(loss, global_step=global_step)
sess = tf.Session()#config=tf.ConfigProto(intra_op_parallelism_threads = 12))
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess.run(init)
validation_feed_dict = {img_placeholder:validation_X,
labels_placeholder:validation_Y,
train_placeholder:False}
step = -1
steps_between_validations = 10
# get decimal places needed to format confusion matrix
fmtLen = int(math.ceil(math.log(max(minibatch_size, VALIDATION_SIZE),10)))
train_ops = [loss, train_op] + model.getTrainOps()
best_acc = 0.0
print(" epoch batch step tr.sec loss vl.sec tr.acc vl.acc vl.sec tr.cmat vl.cmat")
sys.stdout.flush()
for epoch in range(3):
util.shuffle_data(training_X, training_Y)
next_sample_idx = -minibatch_size
for batch in range(batches_per_epoch):
step += 1
next_sample_idx += minibatch_size
X=training_X[next_sample_idx:(next_sample_idx+minibatch_size),:]
Y=training_Y[next_sample_idx:(next_sample_idx+minibatch_size),:]
train_feed_dict = {img_placeholder:X,
labels_placeholder:Y,
train_placeholder:True}
t0 = time.time()
ndarr_train_ops = sess.run(train_ops, feed_dict=train_feed_dict)
train_loss = ndarr_train_ops[0]
train_time = time.time()-t0
msg = " %5d %5d %5d %6.1f %6.3f" % \
(epoch, batch, step, train_time, train_loss)
if step % steps_between_validations == 0:
t0 = time.time()
train_acc, cmat_train_rows = util.get_acc_cmat_for_msg(sess, predict_op, train_feed_dict, Y, fmtLen)
valid_acc, cmat_valid_rows = util.get_acc_cmat_for_msg(sess, predict_op, validation_feed_dict, validation_Y, fmtLen)
valid_time = time.time()-t0
savemsg = ''
if valid_acc > best_acc:
save_path = saver.save(sess, saved_model)
best_acc = valid_acc
savemsg = ' ** saved in %s' % save_path
print('-'*80)
print('%s %6.1f %5.1f%% %5.1f%% %6.1f | %s | %s | %s' %
(msg, valid_time, train_acc*100.0, valid_acc*100.0,
valid_time, cmat_train_rows[0], cmat_valid_rows[0], savemsg))
for row in range(1,len(cmat_train_rows)):
print('%s | %s | %s |' %(' '*(5+6+6+7+7+7+6+6+10),
cmat_train_rows[row],
cmat_valid_rows[row]))
else:
print(msg)
sys.stdout.flush()
def predict(predict_files, saved_model):
t0 = time.time()
numOutputs = 2
Xall, Yall = util.readData(train_files, 'xtcavimg', 'lasing', 'tf', numOutputs)
read_time = time.time()-t0
minibatch_size = 64
print("-- read %d samples for prediction" % len(Xall))
sys.stdout.flush()
img_placeholder = tf.placeholder(tf.int16,
shape=(None,363,284,1),
name='img')
train_placeholder = tf.placeholder(tf.bool, name='trainflag')
model = TFModel.build_model(img_placeholder, train_placeholder, numOutputs=2)
predict_op = tf.nn.softmax(model.final_logits)
sess = tf.Session()#config=tf.ConfigProto(intra_op_parallelism_threads = 12))
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess.run(init)
saver.restore(sess, saved_model)
print("restored model from %s" % saved_model)
sys.stdout.flush()
# get decimal places needed to format confusion matrix
fmtLen = int(math.ceil(math.log(minibatch_size,10)))
idx = -minibatch_size
Ypred = np.zeros(Yall.shape, dtype=np.float32)
while idx + minibatch_size < len(Xall):
idx += minibatch_size
X=Xall[idx:(idx+minibatch_size)]
Y=Yall[idx:(idx+minibatch_size)]
feed_dict={img_placeholder:X,
train_placeholder:False}
Ypred[idx:(idx+minibatch_size)] = sess.run(predict_op, feed_dict=feed_dict)
cmat = util.get_confusion_matrix_one_hot(Ypred, Yall)
acc, cmat_rows = util.get_acc_cmat_for_msg_from_cmat(cmat, 3)
print("Ran predictions. Accuracy: %.2f %d samples" % (acc, len(Ypred)))
for row in cmat_rows:
print(row)
sys.stdout.flush()
def guided_backprop(predict_files, saved_model):
import matplotlib as mpl
mpl.rcParams['backend'] = 'TkAgg'
import matplotlib.pyplot as plt
plt.ion()
plt.figure()
plt.show()
t0 = time.time()
numOutputs = 2
Xall, Yall = util.readData(predict_files, 'xtcavimg', 'lasing', 'tf', numOutputs)
read_time = time.time()-t0
print("-- read %d samples for prediction" % len(Xall))
sys.stdout.flush()
img_placeholder = tf.placeholder(tf.int16,
shape=(None,363,284,1),
name='img')
train_placeholder = tf.placeholder(tf.bool, name='trainflag')
model = TFModel.build_model(img_placeholder, train_placeholder, numOutputs=2)
predict_op = tf.nn.softmax(model.final_logits)
sess = tf.Session()#config=tf.ConfigProto(intra_op_parallelism_threads = 12))
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess.run(init)
saver.restore(sess, saved_model)
print("restored model from %s" % saved_model)
sys.stdout.flush()
for idx in range(len(Xall)):
X=Xall[idx:idx+1]
Y=Yall[idx:idx+1]
feed_dict={img_placeholder:X,
train_placeholder:False}
Ypred = sess.run(predict_op, feed_dict=feed_dict)
class_pred = np.argmax(Ypred)
backprop_img_predicted_label = model.guided_back_prop(sess, X, class_pred)[:,:,0]
plt.subplot(1,2,1)
plt.imshow(X[0,:,:,0], interpolation='none', origin='lower')
truth=0
if Y[0,1]: truth=1
plt.title('raw img %d. pred=%.2f %.2f truth=%d' % (idx, Ypred[0,0], Ypred[0,1], truth))
plt.subplot(1,2,2)
plt.imshow(backprop_img_predicted_label, interpolation='none', origin='lower')
if idx == 0:
plt.colorbar()
plt.title("guided backprop on predicted label")
plt.pause(.1)
def with_graph(train_files, validation_files, predict_files, saved_model, cmd):
if cmd == 'train':
train(train_files, validation_files, saved_model)
elif cmd == 'predict':
predict(predict_files, saved_model)
elif cmd == 'gbprop':
guided_backprop(predict_files, saved_model)
else:
raise Exception(HELP)
if __name__ == '__main__':
HELP = '''usage: %s cmd, where cmd is one of 'predict', 'train' or 'gbprop'.''' % os.path.basename(__file__)
assert len(sys.argv)==2, "no command given: %s" % HELP
print("-- imports done, starting main --")
cmd = sys.argv[1].lower().strip()
saved_model = 'tf_saved_model'
train_files = [
# nolasing files
'amo86815_mlearn-r069-c0011.h5',
'amo86815_mlearn-r069-c0012.h5',
'amo86815_mlearn-r069-c0013.h5',
'amo86815_mlearn-r069-c0016.h5',
'amo86815_mlearn-r069-c0018.h5',
# lasing files
'amo86815_mlearn-r070-c0009.h5',
'amo86815_mlearn-r070-c0014.h5',
'amo86815_mlearn-r070-c0016.h5',
'amo86815_mlearn-r070-c0017.h5',
'amo86815_mlearn-r070-c0019.h5']
validation_files = [
# nolasing files
'amo86815_mlearn-r069-c0031.h5',
# lasing files
'amo86815_mlearn-r070-c0049.h5']
predict_files = ['amo86815_pred-r073-c0121.h5']
with tf.Graph().as_default():
with_graph(train_files, validation_files, predict_files, saved_model, cmd)