def main(_):
# Import data
###mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
print("starting to load data...")
x2 = pickle.load(open( "T1_GD_all_x_no_normalization.p", "rb" ))
print("x2 loaded.")
y2 = pickle.load(open( "T1_GD_all_y_no_normalization.p", "rb" ))
print("y2 loaded.")
validate_x2 = pickle.load(open( "T1_GD_validation_x_no_normalization_aggregated_.p", "rb" ))
print("validate_x2 loaded.")
validate_y2 = pickle.load(open( "T1_GD_validation_y_no_normalization_aggregated.p", "rb" ))
print("validate_y2 loaded.")
validate_x2_nonaggregated = pickle.load(open( "T1_GD_all__validation_x_no_normalization.p", "rb" ))
print("validate_x2 loaded.")
validate_y2_nonaggregated = pickle.load(open( "T1_GD_all__validation_y_no_normalization.p", "rb" ))
print("validate_y2 loaded.")
number_epochs = sys.argv[0]
kernal_size = sys.argv[2]
data_set_all = DataSet(x2,y2, fake_data=False)
validation_set_all = DataSet(validate_x2_nonaggregated, validate_y2_nonaggregated, fake_data=False)
# Create the convolutional model
x = tf.placeholder(tf.float32, [None, 65536])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 3])
# Build the graph for the deep net
# with tf.device('/gpu:2'):
y_conv, keep_prob, saver = deepnn(x)
print(keep_prob)
#plt.imshow(mnist.test.images[0].reshape(28,28))
#print(type(mnist.test.images))
#print(mnist.test.images.shape)
#plt.show()
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#grads = new_optimizer.compute_gradients(cross_entropy)
data_points = []
avg_loss = 0
total_loss = 0
avg_validation_loss = 0
total_validation_loss = 0
batch_size = sys.argv[1]
batches_completed = 0
validation_batches_completed = 0
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
output_file= open("T1_GD_validation_loss_file_no_normalization_"+epoch_size+"_epochs_"+kernal_size+"_kernalsize_"+batch_size+"_batchsize.txt","w+")
with tf.Session(config = config) as sess:
sess.run(tf.global_variables_initializer())
# sess.graph.finalize()
for i in range(5000):
batch_x, batch_y = data_set_all.next_batch(batch_size)
for batch_slice in batch_x:
batch_slice = numpy.reshape(batch_slice, (256, 256))
batch_slice = random_alteration(batch_slice)
batch_slice = numpy.reshape(batch_slice, 65536)
batches_completed += 1
loss = sess.run(cross_entropy, feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.5})
total_loss += loss
new_avg_loss = total_loss/batches_completed
if(new_avg_loss>avg_loss and batches_completed != 1):
avg_loss = new_avg_loss
# break
avg_loss = new_avg_loss
data_points.append(loss)
if i % 1000 == 0:
validation_batch_x, validation_batch_y = validation_set_all.next_batch(batch_size)
validation_batches_completed+=1
train_accuracy = accuracy.eval(feed_dict={x: validation_batch_x, y_: validation_batch_y, keep_prob: 1.0})
validation_loss = cross_entropy.eval(feed_dict={x: validation_batch_x, y_: validation_batch_y, keep_prob: 1.0})
total_validation_loss += validation_loss
new_avg_validation_loss = total_validation_loss/validation_batches_completed
if(new_avg_validation_loss>avg_validation_loss and batches_completed!=1):
avg_validation_loss = new_avg_validation_loss
avg_validation_loss = new_avg_validation_loss
output_file.write("Validation loss at i = %d is %g\n" % (i, avg_validation_loss))
total_times = 0.0
total_accuracy = 0.0
prediction=tf.argmax(y_conv,1)
probabilities=tf.nn.softmax(y_conv)
probs_array = []
condensed_y = []
for j in range(len(validate_x2)):
#print(test_x2[i])
#print(test_y2[i])
temp3 = accuracy.eval(feed_dict={x: validate_x2[j], y_: validate_y2[j], keep_prob: 1.0})
print('test accuracy %g' % temp3)
total_accuracy = total_accuracy + temp3
total_times = total_times+1
temp4 = prediction.eval(feed_dict={x: validate_x2[j], keep_prob: 1.0}, session=sess)
print("predictions", temp4)
probability = probabilities.eval(feed_dict={x: validate_x2[j], keep_prob: 1.0}, session=sess)
print(probability)
if j==0:
probs_array = probability.mean(axis=0)
condensed_y = validate_y2[j].mean(axis=0)
continue
probs_array = numpy.vstack([probs_array, probability.mean(axis=0)])
condensed_y = numpy.vstack([condensed_y, validate_y2[j].mean(axis=0)])
fpr = dict()
tpr = dict()
roc_auc = dict()
for j in range(3):
fpr[j], tpr[j], _ = roc_curve(condensed_y[:, j], probs_array[:, j])
roc_auc[j] = auc(fpr[j], tpr[j])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(condensed_y.ravel(), probs_array.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
output_file.write("ROCs at i = %d are "%i)
for j in range(3):
plt.plot(fpr[j], tpr[j], label='ROC curve of class {0} (area = {1:0.2f})'''.format(j, roc_auc[j]))
output_file.write(str(roc_auc[j])+", ")
output_file.write("\n")
output_file.flush()
print('step %d, training accuracy %g' % (i, train_accuracy))
name = 'T1_GD_testing_with_intermediateROC_no_normalization_epoch_' + str(i)
save_path = saver.save(sess, name)
train_step.run(feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.5})
#testing
print(avg_loss)
output_file.close()
save_path = saver.save(sess, 'T1_GD_testing_with_intermediateROC_no_normalization_final')
def main(_):
# Import data
###mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
print("starting to load data...")
x2 = pickle.load(open("all_x_l2normalization.p", "rb"))
print("x2 loaded.")
y2 = pickle.load(open("all_y_l2normalization.p", "rb"))
print("y2 loaded.")
validate_x2 = pickle.load(open("all__validation_x_l2normalization.p",
"rb"))
print("validate_x2 loaded.")
validate_y2 = pickle.load(open("all__validation_y_l2normalization.p",
"rb"))
print("validate_y2 loaded.")
data_set_all = DataSet(x2, y2, fake_data=False)
validation_set_all = DataSet(validate_x2, validate_y2, fake_data=False)
# Create the convolutional model
x = tf.placeholder(tf.float32, [None, 65536])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 3])
# Build the graph for the deep net
y_conv, keep_prob, saver = deepnn(x)
print(keep_prob)
#plt.imshow(mnist.test.images[0].reshape(28,28))
#print(type(mnist.test.images))
#print(mnist.test.images.shape)
#plt.show()
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#grads = new_optimizer.compute_gradients(cross_entropy)
data_points = []
avg_loss = 0
total_loss = 0
avg_validation_loss = 0
total_validation_loss = 0
batch_size = 10
batches_completed = 0
validation_batches_completed = 0
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
output_file = open("validation_loss_file_l2normalization.txt", "w+")
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
# sess.graph.finalize()
for i in range(1000000):
batch_x, batch_y = data_set_all.next_batch(batch_size)
for batch_slice in batch_x:
batch_slice = numpy.reshape(batch_slice, (256, 256))
batch_slice = random_alteration(batch_slice)
batch_slice = numpy.reshape(batch_slice, 65536)
batches_completed += 1
loss = sess.run(cross_entropy,
feed_dict={
x: batch_x,
y_: batch_y,
keep_prob: 0.5
})
total_loss += loss
new_avg_loss = total_loss / batches_completed
if (new_avg_loss > avg_loss and batches_completed != 1):
avg_loss = new_avg_loss
# break
avg_loss = new_avg_loss
data_points.append(loss)
if i % 10000 == 0:
validation_batch_x, validation_batch_y = validation_set_all.next_batch(
batch_size)
validation_batches_completed += 1
train_accuracy = accuracy.eval(feed_dict={
x: validation_batch_x,
y_: validation_batch_y,
keep_prob: 1.0
})
validation_loss = cross_entropy.eval(feed_dict={
x: validation_batch_x,
y_: validation_batch_y,
keep_prob: 1.0
})
total_validation_loss += validation_loss
new_avg_validation_loss = total_validation_loss / validation_batches_completed
if (new_avg_validation_loss > avg_validation_loss
and batches_completed != 1):
avg_validation_loss = new_avg_validation_loss
avg_validation_loss = new_avg_validation_loss
output_file.write("Validation loss at i = %d is %g\n" %
(i, avg_validation_loss))
output_file.flush()
print('step %d, training accuracy %g' % (i, train_accuracy))
name = 'my-model_testing_l2normalization_epoch_' + str(i)
save_path = saver.save(sess, name)
train_step.run(feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.5})
#testing
print(avg_loss)
output_file.close()
save_path = saver.save(sess, 'my-model_testing_l2normalization_final')
