# 6. Output
_O = [tf.matmul(x, _W['out']) + _b['out'] for x in _LSTM_O]
_O = tf.pack(_O)
# Return!
return {
'X': _X,
'H': _H,
'Hsplit': _Hsplit,
'LSTM_O': _LSTM_O,
'LSTM_S': _LSTM_S,
'O': _O
}
# Load MNIST, our beloved friend
mnist = load_data.read_data_sets("/home/a/workspace/ssd/DataSets/mnist_2/",\
"/home/a/workspace/ssd/DataSets/mnist_2_test/",one_hot=False)
trainimgs, trainlabels, testimgs, testlabels = mnist.train.images,\
mnist.train.labels,\
mnist.test.images,\
mnist.test.labels
ntrain, ntest, dim, nclasses \
= trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print "ntrain: ", ntrain
print "dim: ", dim
print "nclasses: ", nclasses
nclasses = 11
print("MNIST loaded")
# Training params
outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=init_state, time_major=False)
outputs = tf.unstack(tf.transpose(outputs, [1,0,2]))
results=[]
for i in range(len(outputs)) :
result = tf.matmul(outputs[i], weights['out']) + biases['out'] # shape = (128, 10)
results.append(result)
return results
# Load MNIST, our beloved friend
mnist = load_data.read_data_sets("1",\
"2",one_hot=False)
trainimgs, trainlabels, testimgs, testlabels = mnist.train.images,\
mnist.train.labels,\
mnist.test.images,\
mnist.test.labels
nclasses = 36
print ("MNIST loaded")
# Training params
training_epochs = 4000
batch_size = 1
display_step = 1
#learning_rate = 0.001
learning_rate = 0.001
import load_data
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
np.random.seed(100)
# return DataSet class
data = load_data.read_data_sets(one_hot=True)
# get train data and labels by batch size
train_x, train_label = data.train.next_batch(84420)
# get test data
test_x = data.test.data
# get test labels
test_labels = data.test.labels
# get sample number
n_samples = data.train.num_examples
# use knn for classification
knn = KNeighborsClassifier(n_neighbors=3)
# train the model
knn.fit(train_x, train_label)
# predict the values
y_pred = knn.predict(test_x)
# print accuracy
print("Accuracy", accuracy_score(y_pred, test_labels) * 100)
def parse_args1():
parser = argparse.ArgumentParser()
parser.add_argument("pr")
#parser.add_argument("-name","--names_print")
#parser.add_argument("-inc","--increments")
#print parser.pr
return parser.parse_args()
args1 = parse_args1()
# Load MNIST, our beloved friend
mnist = load_data.read_data_sets(
"/mnt/d/workspace/ubuntu/workspace/ocr/" + args1.pr,
"/mnt/d/workspace/ubuntu/workspace/ocr/" + args1.pr,
one_hot=True,
validation_size=1)
trainimgs, trainlabels, testimgs, testlabels \
= mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
ntrain, ntest, dim, nclasses \
= trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print("MNIST loaded")
# Recurrent neural network
diminput = 28
dimhidden = 128
dimoutput = nclasses
nsteps = 28
weights = {
'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
def sparse_tuple_from(sequences, dtype=np.int32):
"""Create a sparse representention of x.
Args:
sequences: a list of lists of type dtype where each element is a sequence
Returns:
A tuple with (indices, values, shape)
"""
indices = []
values = []
for n, seq in enumerate(sequences):
#print "Seq is : ",seq
indices.extend(zip([n]*(seq[0].shape[0]), xrange((seq[0].shape[0])))
#print "length is : ",seq[0].shape
values.extend([seq[0][0]])
values.extend([seq[0][0]])
indices = np.asarray(indices, dtype=np.int64)
values = np.asarray(values, dtype=dtype)
shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1]+1], dtype=np.int64)
return indices, values, shape
def _RNN(_X, _istate, _W, _b, _nsteps, _name):
# 1. Permute input from [batchsize, nsteps, diminput] => [nsteps, batchsize, diminput]
_X = tf.transpose(_X, [1, 0, 2])
# 2. Reshape input to [nsteps*batchsize, diminput]
_X = tf.reshape(_X, [-1, diminput])
# 3. Input layer => Hidden layer
_H = tf.matmul(_X, _W['hidden']) + _b['hidden']
# 4. Splite data to 'nsteps' chunks. An i-th chunck indicates i-th batch data
_Hsplit = tf.split(0, _nsteps, _H)
# 5. Get LSTM's final output (_O) and state (_S)
# Both _O and _S consist of 'batchsize' elements
with tf.variable_scope(_name):
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(dimhidden, forget_bias=1.0)
_LSTM_O, _LSTM_S = tf.nn.rnn(lstm_cell, _Hsplit, initial_state=_istate)
# 6. Output
_O = [tf.matmul(x, _W['out']) + _b['out'] for x in _LSTM_O]
_O = tf.pack(_O)
# Return!
return {
'X': _X, 'H': _H, 'Hsplit': _Hsplit,
'LSTM_O': _LSTM_O, 'LSTM_S': _LSTM_S, 'O': _O
}
# Load MNIST, our beloved friend
#mnist = input_data.read_data_sets("../../DataSets",one_hot=False)
mnist = load_data.read_data_sets("/home/a/workspace/ssd/DataSets/mnist_2/",\
"/home/a/workspace/ssd/DataSets/mnist_2/",one_hot=False)
trainimgs, trainlabels, testimgs, testlabels = mnist.train.images,\
mnist.train.labels,\
mnist.test.images,\
mnist.test.labels
ntrain, ntest, dim = trainimgs.shape[0],\
testimgs.shape[0],\
trainimgs.shape[1],\
#trainlabels.shape[1]
nclasses = 10
print "ntrain: ",ntrain
print "ntest : ",ntest
print "dim: ",dim
print "classes: ",trainlabels.shape
print ("MNIST loaded")
# Training params
training_epochs = 1500
batch_size = 10
display_step = 1
learning_rate = 0.01
# Recurrent neural network params
diminput = 28
dimhidden = 128
# here we add the blank label
dimoutput = nclasses + 1
nsteps = 28
graph = tf.Graph()
with graph.as_default():
weights = {
'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
biases = {
'hidden': tf.Variable(tf.random_normal([dimhidden])),
'out': tf.Variable(tf.random_normal([dimoutput]))
}
#**************************************************
# will be used in CTC_LOSS
x = tf.placeholder(tf.float32, [None, nsteps, diminput])
shape_x = tf.shape(x)
istate = tf.placeholder(tf.float32, [None, 2*dimhidden]) #state & cell => 2x n_hidden
# [batch_size , 1]
y = tf.sparse_placeholder(tf.int32)
# 1d array of size [batch_size]
# Seq len indicates the quantity of true data in the input, since when working with batches we have to pad with zeros to fit the input in a matrix
seq_len = tf.placeholder(tf.int32, [None])
myrnn = _RNN(x, istate, weights, biases, nsteps, 'basic')
pred = myrnn['O']
# [28 25 11] [nsteps, batch_size,num_nclass]
shape_Pred = tf.shape(pred)
#**************************************************
# we add ctc module
loss = ctc.ctc_loss(pred, y, seq_len)
shape_y = tf.shape(y)
cost = tf.reduce_mean(loss)
# Adam Optimizer
optm = tf.train.AdamOptimizer(learning_rate).minimize(cost)
#Decode the best path
decoded, log_prob = ctc.ctc_greedy_decoder(pred, seq_len)
accr = tf.reduce_mean(tf.edit_distance(tf.cast(decoded[0], tf.int32), y))
init = tf.initialize_all_variables()
print ("Network Ready!")
with tf.Session(graph=graph) as sess:
sess.run(init)
summary_writer = tf.train.SummaryWriter('./logs/', graph=sess.graph)
print ("Start optimization")
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_xs = batch_xs.reshape((batch_size, nsteps, diminput))
#print "shape of batch_xs is : ",batch_xs.shape
# Fit training using batch data
feed_dict={x: batch_xs, y: sparse_tuple_from([[value] for value in batch_ys]), \
istate: np.zeros((batch_size, 2*dimhidden)), \
seq_len: [nsteps for _ in xrange(batch_size)]}
_, batch_cost = sess.run([optm, cost], feed_dict=feed_dict)
# Compute average loss
avg_cost += batch_cost*batch_size
#print "COST_pred shape is :",pred.shape
avg_cost /= len(trainimgs)
# Display logs per epoch step
if epoch % display_step == 0:
print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
train_acc = sess.run(accr, feed_dict=feed_dict)
#prediction = sess.run(decoded[0],feed_dict=feed_dict)
print " "
print "**************************XXXXXXXXXXXXXXXXX: "
print "Shape of x is : ",sess.run(shape_x,feed_dict=feed_dict)
print "Shape of y is : ",sess.run(shape_y,feed_dict=feed_dict)
print "Shape of pred : ",sess.run(shape_Pred,feed_dict=feed_dict)
#print "prediction is : ",prediction
print (" Training label error rate: %.3f" % (train_acc))
testimgs = testimgs.reshape((ntest, nsteps, diminput))
feed_dict={x: testimgs, y: sparse_tuple_from([[value] for value in testlabels]), istate: np.zeros((ntest, 2*dimhidden)), seq_len: [nsteps for _ in xrange(len(testimgs))]}
test_acc = sess.run(accr, feed_dict=feed_dict)
print (" Test label error rate: %.3f" % (test_acc))
p_xs, p_ys = mnist.train.next_batch(batch_size)
p_xs = p_xs.reshape((batch_size,nsteps,diminput))
'''
feed_dict ={x:p_xs,y:sparse_tuple_from([[value] for value in p_ys]),
istate: np.zeros((ntest, 2*dimhidden)),
seq_len: [nsteps for _ in xrange(batch_size)]}
prediction = sess.run(decoded[0],feed_dict=feed_dict)
print "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX: ",prediction
'''
print ("Optimization Finished.")
def main():
# return dataset from load_data function in load_data.py script
data = load_data.read_data_sets(one_hot=True)
# get train data and labels by batch size
# training = 84420 and testing = 58128
BATCH_SIZE = 84420
train_x, train_labels = data.train.next_batch(BATCH_SIZE)
# get test data
test_x = data.test.data
# get test labels
test_labels = data.test.labels
# to get class ratio in the testing dataset
total_count = 0
pos_count = 0
neg_count = 0
neu_count = 0
#Label formation
train_label = []
for i in range(len(train_labels)):
total_count = total_count + 1
if train_labels[i, 0] == 1:
# If emotion is Positive set label to 1
train_label.append(1)
pos_count = pos_count + 1
if train_labels[i, 1] == 1:
# If emotion is neutral set label to 0
train_label.append(0)
neu_count = neu_count + 1
if train_labels[i, 2] == 1:
# If emotion is negative set label to -1
train_label.append(-1)
neg_count = neg_count + 1
test_label = []
for i in range(len(test_labels)):
total_count = total_count + 1
if test_labels[i, 0] == 1:
# If emotion is Positive set label to 1
test_label.append(1)
pos_count = pos_count + 1
if test_labels[i, 1] == 1:
# If emotion is neutral set label to 0
test_label.append(0)
neu_count = neu_count + 1
if test_labels[i, 2] == 1:
# If emotion is negative set label to -1
test_label.append(-1)
neg_count = neg_count + 1
# Merge train and test dataset
train_np = np.array(train_x)
test_np = np.array(test_x)
dataset = np.concatenate((train_np, test_np))
# Merge train and test labels
train_label_np = np.array(train_label)
test_label_np = np.array(test_label)
dataset_label = np.concatenate((train_label_np, test_label_np))
# Starting timer
start_time_lda = time.time()
# Implementing pre processing steps such as data augmentation and feature extraction
train_fe, test_fe = preprocessing_methods(dataset, dataset_label)
# Implementing CNN model for classification
convolution_model(train_fe, train_labels, test_fe, test_labels,
start_time_lda)
#!/usr/bin/env python
# coding=utf-8
# Import packages
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import numpy as np
import load_data
#import matplotlib.pyplot as plt
#matplotlib inline
print("Packages imported")
# Load MNIST, our beloved friend
mnist = load_data.read_data_sets("/home/moon/work/shendu/mnist_data",
"/home/moon/work/shendu/mnist_data",
one_hot=False,
validation_size=5000)
trainimgs, trainlabels, testimgs, testlabels \
= mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
ntrain, ntest, dim, nclasses \
= trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print("MNIST loaded")
# Recurrent neural network
diminput = 80
dimhidden = 128
dimoutput = nclasses
nsteps = 120
weights = {
'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
#!/usr/bin/env python
# coding=utf-8
# Import packages
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import numpy as np
import load_data
#import matplotlib.pyplot as plt
#matplotlib inline
print("Packages imported")
# Load MNIST, our beloved friend
mnist = load_data.read_data_sets("/home/a/workspace/ssd/DataSets/mnist",
"/home/a/workspace/ssd/DataSets/mnist_test",
one_hot=False,
validation_size=5000)
trainimgs, trainlabels, testimgs, testlabels \
= mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
ntrain, ntest, dim, nclasses \
= trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print("MNIST loaded")
# Recurrent neural network
diminput = 80
dimhidden = 128
dimoutput = nclasses
nsteps = 120
weights = {
'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
def run_training():
"""Train, evaluate and test the model
"""
#get the data and format it as DataSet tuples
data_sets = ld.read_data_sets(FLAGS.train_dir, FLAGS.train_file)
feature_groups = data_sets.train.feature_groups
feature_size = data_sets.train.feature_size
dm.FEATURE_SIZE = feature_size #set the feature size of the datasets
if FLAGS.skip_eval == 'NO':
#output the evaluation results
if not os.path.exists(FLAGS.evaluation_dir):
os.makedirs(FLAGS.evaluation_dir)
eval_output = open(FLAGS.evaluation_dir + "/" + FLAGS.evaluation_file,
"w")
para_info = get_parameters()
eval_output.write("#" + para_info + "\n")
eval_output.write("train_steps\teval_data_type\tloss\tcindex\n")
#output the evaluation cindex detail
if not os.path.exists(FLAGS.evaluation_dir):
os.makedirs(FLAGS.evaluation_dir)
if FLAGS.output_evaluation_cindex == 'YES':
eval_cindex_output = open(
FLAGS.evaluation_dir + "/" + FLAGS.evaluation_cindex_file, "w")
eval_cindex_output.write(
"eval_type\tdates\tdates_predicted\tcensorship\tcindex\n")
#Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
#Generate input placeholder
feature_pl, at_risk_pl, date_pl, censor_pl = placeholder_inputs(
FLAGS.batch_size)
# Build the graph and get the prediction from the inference model
isTrain_pl = tf.placeholder(
tf.bool, shape=()
) #boolean value to check if it is during training optimization process
if FLAGS.model == 'NN':
hidden_nodes = [int(x) for x in FLAGS.hidden_nodes.split(",")]
#inf_output_pl=tf.cond(isTrain_pl,inf_train,inf_nontrain)
inf_output_pl = dm.inference(feature_pl, hidden_nodes,
FLAGS.activation,
FLAGS.dropout_keep_rate, isTrain_pl)
#inf_output_pl=dm.inference(feature_pl,hidden_nodes,FLAGS.activation)
elif FLAGS.model == 'linear':
#alpha should equal to 1
inf_output_pl = dm.inference_linear(feature_pl)
# Add to the Graph the Ops for loss calculation.
#loss=dm.loss(inf_output_pl,at_risk_pl,censor_pl,feature_groups,FLAGS.batch_size,FLAGS.alpha,FLAGS.scale,FLAGS.reg_type)
loss = dm.loss(inf_output_pl, censor_pl, feature_groups,
FLAGS.batch_size, FLAGS.alpha, FLAGS.scale, FLAGS.delta,
FLAGS.reg_type)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = dm.training(loss, FLAGS.initial_learning_rate)
# Add evaluation to the Graph
#cindex_pl=dm.evaluation(inf_output,date_pl,censor_pl)
# Build the summary Tensor based on the TF collection of Summaries.
#summary=tf.summary.merge_all()
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
#summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# Run the Op to initialize the variables.
sess.run(init)
# Begin the training loops.
for step in range(FLAGS.max_steps):
start_time = time.time()
#fill the data
feed_dict = fill_feed_dict(
data_sets.train.next_batch(FLAGS.batch_size), feature_pl,
at_risk_pl, date_pl, censor_pl)
feed_dict[isTrain_pl] = True
#run training, update parameters
_, loss_value, output = sess.run([train_op, loss, inf_output_pl],
feed_dict=feed_dict)
#_, loss_value, loss2_value , output= sess.run([train_op,loss, loss2 ,inf_output_pl],feed_dict=feed_dict) #TEST
#print("output type:"+str(type(output)))
#print("output shape:"+str(output.shape))
duration = time.time() - start_time
if step % 5 == 0:
pass
# Print status to stdout.
#print('Step %d: loss = %.2f (%.3f sec) : output max = %.2f' % (step, loss_value, duration, output.max()))
# Update the events file.
#summary_str = sess.run(summary, feed_dict=feed_dict)
#summary_writer.add_summary(summary_str, step)
#summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 5 == 0 or (step + 1) == FLAGS.max_steps:
print('Step: %d , duration: %.3f sec' % (step + 1, duration))
#Save the variables to disk
checkpoint_file = os.path.join(FLAGS.saver_file_dir,
FLAGS.saver_file_prefix)
saver.save(sess, checkpoint_file, global_step=step)
if FLAGS.skip_eval == 'NO':
if FLAGS.output_evaluation_cindex != 'YES':
eval_cindex_output = 0
# Evaluate agianst the traing data.
print("Training data evaluation:")
do_eval(sess, eval_cindex_output, eval_output,
"training_data", step + 1, loss, inf_output_pl,
feature_pl, at_risk_pl, date_pl, censor_pl,
data_sets.train, isTrain_pl, False)
print("Validation data evaluation:")
do_eval(sess, eval_cindex_output, eval_output,
"validation_data", step + 1, loss, inf_output_pl,
feature_pl, at_risk_pl, date_pl, censor_pl,
data_sets.validation, isTrain_pl, False)
print("Testing data evaluation:")
do_eval(sess, eval_cindex_output, eval_output,
"testing_data", step + 1, loss, inf_output_pl,
feature_pl, at_risk_pl, date_pl, censor_pl,
data_sets.test, isTrain_pl, False)
if FLAGS.skip_eval == 'NO':
eval_output.close()
if FLAGS.output_evaluation_cindex == 'YES':
eval_cindex_output.close()