#start_checkpoint = '/home/zhangs/tensorflow-master/tensorflow/examples/speech_lyc/tmp/speech_commands_train/hd5_train_2words_8bit_mixup_16fea_bnnv2/fixed_point_bnn_v2/fixed_point_bnn_v2.ckpt-9800'
start_checkpoint = '/home/zhangs/tensorflow-master/tensorflow/examples/speech_lyc/tmp/speech_commands_train/h5_40_20ms16_1word_happy_dscv1_mix1Q6nobias/dscv1/dscv1.ckpt-5800'
pbs_path = ''
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
len(new_features_input.prepare_words_list_my(wanted_words.split(','))),
sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms, dct_coefficient_count)
audio_processor = new_features_input.AudioProcessor(
data_dir, silence_percentage,
wanted_words.split(','), validation_percentage,
testing_percentage)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
fingerprint_input = tf.placeholder(
# tf.float32, [None, fingerprint_size], name='fingerprint_input') #
tf.float32, [None, 20,7,64], name='fingerprint_input') #
fingerprint_input_raw = tf.placeholder(
tf.float32, [None, fingerprint_size], name='fingerprint_input_raw') #
#tf.float32, [None, 20,7,64], name='fingerprint_input') #
ground_truth_input = tf.placeholder(
tf.float32, [None, label_count], name='groundtruth_input')
###########################
dscv1model = rebuild_model.paras(start_checkpoint)
logits,lconv1,lconv1bn,ldsc1,ldsc1bn,ldsc1pw,ldsc1pwbn,ldsc2,ldsc2bn,ldsc2pw,ldsc2pwbn,ldsc3,ldsc3bn,ldsc3pw,ldsc3pwbn,\
def main(_):
best_acc = 0
best_step = 0
best_acc_istrain = 0
best_step_istrain = 0
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
sess = tf.InteractiveSession()
# Begin by making sure we have the training data we need. If you already have
# training data of your own, use `--data_url= ` on the command line to avoid
# downloading.
model_settings = models.prepare_model_settings(
len(new_features_input.prepare_words_list_my(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count)
audio_processor = new_features_input.AudioProcessor(
FLAGS.data_dir, FLAGS.silence_percentage,
FLAGS.wanted_words.split(','), FLAGS.validation_percentage,
FLAGS.testing_percentage)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
training_steps_list = list(map(int, FLAGS.how_many_training_steps.split(',')))
learning_rates_list = list(map(float, FLAGS.learning_rate.split(',')))
if len(training_steps_list) != len(learning_rates_list):
raise Exception(
'--how_many_training_steps and --learning_rate must be equal length '
'lists, but are %d and %d long instead' % (len(training_steps_list),
len(learning_rates_list)))
##############################################
############tensorflow modules##########
fingerprint_input = tf.placeholder(
tf.float32, [None, fingerprint_size], name='fingerprint_input')
# ############ 模型创建 ##########
istrain = tf.placeholder(tf.bool, name='istrain')
logits,conv1,conv1bn,dsc1,dsc1bn,dsc1pw,dsc1pwbn,dsc2,dsc2bn,dsc2pw,dsc2pwbn,dsc3,dsc3bn,dsc3pw,dsc3pwbn,\
dsc4,dsc4bn,dsc4pw,dsc4pwbn,fc= models.create_model(
fingerprint_input,
model_settings,
FLAGS.model_architecture,
is_training=istrain)
############ 模型创建 ##########
# logits, dropout_prob= models.create_model(
# fingerprint_input,
# model_settings,
# FLAGS.model_architecture,
# is_training=True)
# Define loss and optimizer
############ 真实值 ##########
ground_truth_input = tf.placeholder(
tf.float32, [None, label_count], name='groundtruth_input')
# Optionally we can add runtime checks to spot when NaNs or other symptoms of
# numerical errors start occurring during training.
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
# Create the back propagation and training evaluation machinery in the graph.
############ 交叉熵计算 ##########
# with tf.name_scope('cross_entropy'):
# cross_entropy_mean = tf.reduce_mean(
# tf.nn.softmax_cross_entropy_with_logits(
# labels=ground_truth_input, logits=logits)) + beta*loss_norm
with tf.name_scope('cross_entropy'):
cross_entropy_mean = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
labels=ground_truth_input, logits=logits))
tf.summary.scalar('cross_entropy', cross_entropy_mean)
############ 学习率、准确率、混淆矩阵 ##########
# learning_rate_input 学习率输入（tf.placeholder）
# train_step 训练过程 （优化器）
# predicted_indices 预测输出索引
# expected_indices 实际希望输出索引
# correct_prediction 正确预测矩阵
# confusion_matrix 混淆矩阵
# evaluation_step 正确分类概率（每个阶段）
# global_step 全局训练阶段
# increment_global_step 全局训练阶段递增
learning_rate_input = tf.placeholder(
tf.float32, [], name='learning_rate_input')
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(
learning_rate_input).minimize(cross_entropy_mean)
# with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
# learning_rate_input = tf.placeholder(
# tf.float32, [], name='learning_rate_input')
# # train_step = tf.train.GradientDescentOptimizer(
# # learning_rate_input).minimize(cross_entropy_mean)
# with tf.control_dependencies(update_ops):
# train_step = tf.train.AdamOptimizer(
# learning_rate_input).minimize(cross_entropy_mean)
predicted_indices = tf.argmax(logits, 1)
expected_indices = tf.argmax(ground_truth_input, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(
expected_indices, predicted_indices, num_classes=label_count)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc = tf.summary.scalar('accuracy', evaluation_step)
global_step = tf.train.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
saver = tf.train.Saver(tf.global_variables(),max_to_keep=None)# max keep file // moren 5
# Merge all the summaries and write them out to /tmp/retrain_logs (by default)
merged_summaries = tf.summary.merge_all()
validation_merged_summaries = tf.summary.merge([tf.get_collection(tf.GraphKeys.SUMMARIES,'accuracy'),tf.get_collection(tf.GraphKeys.SUMMARIES,'cross_entropy')])
test_summaries = tf.summary.merge([acc])
test_summaries_istrain = tf.summary.merge([tf.get_collection(tf.GraphKeys.SUMMARIES,'accuracy'),tf.get_collection(tf.GraphKeys.SUMMARIES,'cross_entropy')])
#test_summaries_istrain = tf.summary.merge([acc])
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
# validation_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/validation')
test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
test_istrain_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test_istrain')
tf.global_variables_initializer().run()
start_step = 1
if FLAGS.start_checkpoint:
models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint)
start_step = global_step.eval(session=sess)
tf.logging.info('Training from step: %d ', start_step)
# Save graph.pbtxt.
tf.train.write_graph(sess.graph_def, FLAGS.train_dir,
FLAGS.model_architecture + '.pbtxt')
# Save list of words.
with gfile.GFile(
os.path.join(FLAGS.train_dir, FLAGS.model_architecture + '_labels.txt'),
'w') as f:
f.write('\n'.join(audio_processor.words_list))
###
# model1: fc
# model2: conv :940k个parameter
# model3:low_latancy_conv:~~model1
# model4: 750k
# Training loop.
#############################################
######## 主循环 ######
#############################################
training_steps_max = np.sum(training_steps_list)
for training_step in xrange(start_step, training_steps_max + 1):
# Figure out what the current learning rate is.
####### 自动切换学习率 #######
if training_step <12000+1:
learning_rate_value = learning_rates_list[0]*0.02**(training_step/12000)
else:
learning_rate_value = learning_rates_list[0]*0.02 #0.015 12000
training_steps_sum = 0
# for i in range(len(training_steps_list)):
# training_steps_sum += training_steps_list[i]
# if training_step <= training_steps_sum:
# learning_rate_value = learning_rates_list[i]
# break
# Pull the audio samples we'll use for training.
####### audio处理器导入数据 ##################################
##get_data(self, how_many, offset, model_settings, background_frequency,
## background_volume_range, time_shift, mode, sess)
########################################################################
train_fingerprints, train_ground_truth = audio_processor.get_data_my(
FLAGS.batch_size, 0, model_settings ,'training')
#mid = np.abs(np.max(train_fingerprints) + np.min(train_fingerprints)) / 2
#half = np.max(train_fingerprints) - np.min(train_fingerprints)
# train_fingerprints = ((train_fingerprints + mid) / half * 255).astype(int)
train_fingerprints_mix, train_ground_truth_mix = mixup_data(train_fingerprints, train_ground_truth, 1)
train_fingerprints = np.append(train_fingerprints, train_fingerprints_mix, axis=0)
train_ground_truth = np.append(train_ground_truth, train_ground_truth_mix, axis=0)
random_index = list(np.arange(FLAGS.batch_size*2))
np.random.shuffle(random_index)
train_fingerprints = train_fingerprints[random_index, :]
train_ground_truth = train_ground_truth[random_index, :]
train_summary, train_accuracy, cross_entropy_value, _, _ = sess.run(
[
merged_summaries, evaluation_step, cross_entropy_mean, train_step,
increment_global_step
],
feed_dict={
fingerprint_input: train_fingerprints,
ground_truth_input: train_ground_truth,
learning_rate_input: learning_rate_value,
istrain:True
})
train_writer.add_summary(train_summary, training_step)
tf.logging.info('Step #%d: rate %f, accuracy %.1f%%, cross entropy %f' %
(training_step, learning_rate_value, train_accuracy * 100,
cross_entropy_value))
is_last_step = (training_step == training_steps_max)
if (training_step % FLAGS.eval_step_interval) == 0 or is_last_step:
#############################################
######## 测试集重复计算正确率和混淆矩阵 ######
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
test_fingerprints, test_ground_truth = audio_processor.get_data_my(
-1, 0, model_settings,'testing')
#mid = np.abs(np.max(test_fingerprints) + np.min(test_fingerprints)) / 2
#half = np.max(test_fingerprints) - np.min(test_fingerprints)
#test_fingerprints = ((test_fingerprints + mid) / half * 255).astype(int)
final_summary,test_accuracy, conf_matrix = sess.run(
[test_summaries,evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_input: test_ground_truth,
istrain : False
})
final_summary_istrain,test_accuracy_istrain= sess.run(
[test_summaries_istrain,evaluation_step],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_input: test_ground_truth,
istrain : True
})
if test_accuracy > best_acc:
best_acc = test_accuracy
best_step = training_step
if test_accuracy_istrain > best_acc_istrain:
best_acc_istrain = test_accuracy_istrain
best_step_istrain = training_step
test_writer.add_summary(final_summary, training_step)
test_istrain_writer.add_summary(final_summary_istrain, training_step)
tf.logging.info('Confusion Matrix:\n %s' % (conf_matrix))
tf.logging.info('test accuracy = %.1f%% (N=%d)' % (test_accuracy * 100,6882))
tf.logging.info('test_istrain accuracy = %.1f%% (N=%d)' % (test_accuracy_istrain * 100,6882))
tf.logging.info('Best test accuracy before now = %.1f%% (N=%d)' % (best_acc * 100,6882) + ' at step of ' + str(best_step))
tf.logging.info('Best test_istrain accuracy before now = %.1f%% (N=%d)' % (best_acc_istrain * 100,6882) + ' at step of ' + str(best_step_istrain))
# Save the model checkpoint periodically.
if (training_step % FLAGS.save_step_interval == 0 or
training_step == training_steps_max):
checkpoint_path = os.path.join(FLAGS.train_dir + '/'+FLAGS.model_architecture,
FLAGS.model_architecture + '.ckpt')
tf.logging.info('Saving to "%s-%d"', checkpoint_path, training_step)
saver.save(sess, checkpoint_path, global_step=training_step)
print_line = 'Best test accuracy before now = %.1f%% (N=%d)' % (best_acc * 100,6882) + ' at step of ' + str(best_step) + '\n' + \
'Best test_istrain accuracy before now = %.1f%% (N=%d)' % (best_acc_istrain * 100,6882) + ' at step of ' + str(best_step_istrain)
if training_step == training_steps_max:
with open(FLAGS.train_dir + '/' +FLAGS.model_architecture+ '/details.txt', 'w') as f:
f.write(print_line)