def train(images,
labels,
fold,
model_type,
batch_size,
num_epochs,
subj_id=0,
reuse_cnn=False,
dropout_rate=dropout_rate,
learning_rate_default=1e-3,
Optimizer=tf.train.AdamOptimizer,
log_path=log_path):
"""
A sample training function which loops over the training set and evaluates the network
on the validation set after each epoch. Evaluates the network on the training set
whenever the
:param images: input images
:param labels: target labels
:param fold: tuple of (train, test) index numbers
:param model_type: model type ('cnn', '1dconv', 'lstm', 'mix')
:param batch_size: batch size for training
:param num_epochs: number of epochs of dataset to go over for training
:param subj_id: the id of fold for storing log and the best model
:param reuse_cnn: whether to train cnn first, and load its weight for multi-frame model
:return: none
"""
with tf.name_scope('Inputs'):
input_var = tf.placeholder(tf.float32, [None, None, 32, 32, n_colors],
name='X_inputs')
target_var = tf.placeholder(tf.int64, [None], name='y_inputs')
tf_is_training = tf.placeholder(tf.bool, None, name='is_training')
num_classes = len(np.unique(labels))
(X_train,
y_train), (X_val, y_val), (X_test,
y_test) = reformatInput(images, labels, fold)
print('Train set label and proportion:\t',
np.unique(y_train, return_counts=True))
print('Val set label and proportion:\t',
np.unique(y_val, return_counts=True))
print('Test set label and proportion:\t',
np.unique(y_test, return_counts=True))
print('The shape of X_trian:\t', X_train.shape)
print('The shape of X_val:\t', X_val.shape)
print('The shape of X_test:\t', X_test.shape)
print("Building model and compiling functions...")
if model_type == '1dconv':
network = build_convpool_conv1d(input_var,
num_classes,
train=tf_is_training,
dropout_rate=dropout_rate,
name='CNN_Conv1d' + '_sbj' +
str(subj_id))
elif model_type == 'lstm':
network = build_convpool_lstm(input_var,
num_classes,
100,
train=tf_is_training,
dropout_rate=dropout_rate,
name='CNN_LSTM' + '_sbj' + str(subj_id))
elif model_type == 'mix':
network = build_convpool_mix(input_var,
num_classes,
100,
train=tf_is_training,
dropout_rate=dropout_rate,
name='CNN_Mix' + '_sbj' + str(subj_id))
elif model_type == 'cnn':
with tf.name_scope(name='CNN_layer' + '_fold' + str(subj_id)):
network = build_cnn(input_var) # output shape [None, 4, 4, 128]
convpool_flat = tf.reshape(network, [-1, 4 * 4 * 128])
h_fc1_drop1 = tf.layers.dropout(convpool_flat,
rate=dropout_rate,
training=tf_is_training,
name='dropout_1')
h_fc1 = tf.layers.dense(h_fc1_drop1,
256,
activation=tf.nn.relu,
name='fc_relu_256')
h_fc1_drop2 = tf.layers.dropout(h_fc1,
rate=dropout_rate,
training=tf_is_training,
name='dropout_2')
network = tf.layers.dense(h_fc1_drop2,
num_classes,
name='fc_softmax')
# the loss function contains the softmax activation
else:
raise ValueError(
"Model not supported ['1dconv', 'maxpool', 'lstm', 'mix', 'cnn']")
Train_vars = tf.trainable_variables()
prediction = network
with tf.name_scope('Loss'):
l2_loss = tf.add_n(
[tf.nn.l2_loss(v) for v in Train_vars if 'kernel' in v.name])
ce_loss = tf.losses.sparse_softmax_cross_entropy(labels=target_var,
logits=prediction)
_loss = ce_loss + weight_decay * l2_loss
# decay_steps learning rate decay
decay_steps = 3 * (
len(y_train) // batch_size
) # len(X_train)//batch_size the training steps for an epcoh
with tf.name_scope('Optimizer'):
# learning_rate = learning_rate_default * Decay_rate^(global_steps/decay_steps)
global_steps = tf.Variable(0, name="global_step", trainable=False)
learning_rate = tf.train.exponential_decay( # learning rate decay
learning_rate_default, # Base learning rate.
global_steps,
decay_steps,
0.95, # Decay rate.
staircase=True)
optimizer = Optimizer(
learning_rate) # GradientDescentOptimizer AdamOptimizer
train_op = optimizer.minimize(_loss,
global_step=global_steps,
var_list=Train_vars)
with tf.name_scope('Accuracy'):
prediction = tf.argmax(prediction, axis=1)
correct_prediction = tf.equal(prediction, target_var)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Output directory for models and summaries
# choose different path for different model and subject
out_dir = os.path.abspath(
os.path.join(os.path.curdir, log_path,
(model_type + '_' + str(subj_id))))
print("Writing to {}\n".format(out_dir))
# Summaries for loss, accuracy and learning_rate
loss_summary = tf.summary.scalar('loss', _loss)
acc_summary = tf.summary.scalar('train_acc', accuracy)
lr_summary = tf.summary.scalar('learning_rate', learning_rate)
# Train Summaries
train_summary_op = tf.summary.merge(
[loss_summary, acc_summary, lr_summary])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir,
tf.get_default_graph())
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir,
tf.get_default_graph())
# Test summaries
test_summary_op = tf.summary.merge([loss_summary, acc_summary])
test_summary_dir = os.path.join(out_dir, "summaries", "test")
test_summary_writer = tf.summary.FileWriter(test_summary_dir,
tf.get_default_graph())
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, model_type)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if model_type != 'cnn' and reuse_cnn:
# saver for reuse the CNN weight
reuse_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='VGG_NET_CNN')
original_saver = tf.train.Saver(
reuse_vars) # Pass the variables as a list
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
print("Starting training...")
total_start_time = time.time()
best_validation_accu = 0
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
if model_type != 'cnn' and reuse_cnn:
cnn_model_path = os.path.abspath(
os.path.join(os.path.curdir, log_path, ('cnn_' + str(subj_id)),
'checkpoints'))
cnn_model_path = tf.train.latest_checkpoint(cnn_model_path)
print('-' * 20)
print('Load cnn model weight for multi-frame model from {}'.format(
cnn_model_path))
original_saver.restore(sess, cnn_model_path)
stop_count = 0 # count for earlystopping
for epoch in range(num_epochs):
print('-' * 50)
# Train set
train_err = train_acc = train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train,
y_train,
batch_size,
shuffle=False):
inputs, targets = batch
summary, _, pred, loss, acc = sess.run(
[train_summary_op, train_op, prediction, _loss, accuracy],
{
input_var: inputs,
target_var: targets,
tf_is_training: True
})
train_acc += acc
train_err += loss
train_batches += 1
train_summary_writer.add_summary(summary,
sess.run(global_steps))
av_train_err = train_err / train_batches
av_train_acc = train_acc / train_batches
# Val set
summary, pred, av_val_err, av_val_acc = sess.run(
[dev_summary_op, prediction, _loss, accuracy], {
input_var: X_val,
target_var: y_val,
tf_is_training: False
})
dev_summary_writer.add_summary(summary, sess.run(global_steps))
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs,
time.time() - start_time))
fmt_str = "Train \tEpoch [{:d}/{:d}] train_Loss: {:.4f}\ttrain_Acc: {:.2f}"
print_str = fmt_str.format(epoch + 1, num_epochs, av_train_err,
av_train_acc * 100)
print(print_str)
fmt_str = "Val \tEpoch [{:d}/{:d}] val_Loss: {:.4f}\tval_Acc: {:.2f}"
print_str = fmt_str.format(epoch + 1, num_epochs, av_val_err,
av_val_acc * 100)
print(print_str)
# Test set
summary, pred, av_test_err, av_test_acc = sess.run(
[test_summary_op, prediction, _loss, accuracy], {
input_var: X_test,
target_var: y_test,
tf_is_training: False
})
test_summary_writer.add_summary(summary, sess.run(global_steps))
fmt_str = "Test \tEpoch [{:d}/{:d}] test_Loss: {:.4f}\ttest_Acc: {:.2f}"
print_str = fmt_str.format(epoch + 1, num_epochs, av_test_err,
av_test_acc * 100)
print(print_str)
if av_val_acc > best_validation_accu: # early_stoping
stop_count = 0
eraly_stoping_epoch = epoch
best_validation_accu = av_val_acc
test_acc_val = av_test_acc
saver.save(sess,
checkpoint_prefix,
global_step=sess.run(global_steps))
else:
stop_count += 1
if stop_count >= 10: # stop training if val_acc dose not imporve for over 10 epochs
break
train_batches = train_acc = 0
for batch in iterate_minibatches(X_train,
y_train,
batch_size,
shuffle=False):
inputs, targets = batch
acc = sess.run(accuracy, {
input_var: X_train,
target_var: y_train,
tf_is_training: False
})
train_acc += acc
train_batches += 1
last_train_acc = train_acc / train_batches
last_val_acc = av_val_acc
last_test_acc = av_test_acc
print('-' * 50)
print('Time in total:', time.time() - total_start_time)
print("Best validation accuracy:\t\t{:.2f} %".format(
best_validation_accu * 100))
print(
"Test accuracy when got the best validation accuracy:\t\t{:.2f} %".
format(test_acc_val * 100))
print('-' * 50)
print("Last train accuracy:\t\t{:.2f} %".format(last_train_acc * 100))
print("Last validation accuracy:\t\t{:.2f} %".format(last_val_acc *
100))
print("Last test accuracy:\t\t\t\t{:.2f} %".format(last_test_acc *
100))
print('Early Stopping at epoch: {}'.format(eraly_stoping_epoch + 1))
train_summary_writer.close()
dev_summary_writer.close()
test_summary_writer.close()
return [
last_train_acc, best_validation_accu, test_acc_val, last_val_acc,
last_test_acc
]
def train(images, labels, fold, model_type, batch_size=32, num_epochs=5):
"""
A sample training function which loops over the training set and evaluates the network
on the validation set after each epoch. Evaluates the network on the training set
whenever the
:param images: input images
:param labels: target labels
:param fold: tuple of (train, test) index numbers
:param model_type: model type ('cnn', '1dconv', 'maxpool', 'lstm', 'mix')
:param batch_size: batch size for training
:param num_epochs: number of epochs of dataset to go over for training
:return: none
"""
num_classes = len(np.unique(labels))
(X_train,
y_train), (X_val, y_val), (X_test,
y_test) = reformatInput(images, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
# Prepare Theano variables for inputs and targets
input_var = T.TensorType('floatX', ((False, ) * 5))()
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
# Building the appropriate model
if model_type == '1dconv':
network = build_convpool_conv1d(input_var, num_classes)
elif model_type == 'maxpool':
network = build_convpool_max(input_var, num_classes)
elif model_type == 'lstm':
network = build_convpool_lstm(input_var, num_classes, 100)
elif model_type == 'mix':
network = build_convpool_mix(input_var, num_classes, 100)
elif model_type == 'cnn':
input_var = T.tensor4('inputs')
network, _ = build_cnn(input_var)
network = DenseLayer(lasagne.layers.dropout(network, p=.5),
num_units=256,
nonlinearity=lasagne.nonlinearities.rectify)
network = DenseLayer(lasagne.layers.dropout(network, p=.5),
num_units=num_classes,
nonlinearity=lasagne.nonlinearities.softmax)
else:
raise ValueError(
"Model not supported ['1dconv', 'maxpool', 'lstm', 'mix', 'cnn']")
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
reg_factor = 1e-4
l2_penalty = regularize_network_params(network, l2) * reg_factor
loss += l2_penalty
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=0.001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
best_validation_accu = 0
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train,
y_train,
batch_size,
shuffle=False):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val,
y_val,
batch_size,
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
av_train_err = train_err / train_batches
av_val_err = val_err / val_batches
av_val_acc = val_acc / val_batches
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(epoch + 1, num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(av_train_err))
print(" validation loss:\t\t{:.6f}".format(av_val_err))
print(" validation accuracy:\t\t{:.2f} %".format(av_val_acc * 100))
if av_val_acc > best_validation_accu:
best_validation_accu = av_val_acc
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test,
y_test,
batch_size,
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
av_test_err = test_err / test_batches
av_test_acc = test_acc / test_batches
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(av_test_err))
print(" test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
# Dump the network weights to a file like this:
np.savez('weights_lasg_{0}'.format(model_type),
*lasagne.layers.get_all_param_values(network))
print('-' * 50)
print("Best validation accuracy:\t\t{:.2f} %".format(best_validation_accu *
100))
print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
return av_test_acc
def main():
# Load the dataset
print("Loading data...")
data, labels = load_data(filename)
mat = scipy.io.loadmat(subjectsFilename, mat_dtype=True)
subjNumbers = np.squeeze(mat['subjectNum']) # subject IDs for each trial
# Create folds based on subject numbers (for leave-subject-out x-validation)
fold_pairs = []
# If augmentation is selected include augmented data
if augment:
# Aggregate augmented data and labels
data_aug, labels_aug = load_data(filename_aug)
data = np.vstack((data, data_aug))
labels = np.vstack((labels, labels_aug))
# Leave-Subject-Out cross validation
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts))) # Training indices
ts = np.squeeze(np.nonzero(ts))
# Include augmented training data
tr = np.concatenate((tr, tr + subjNumbers.size))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
else:
# Leave-Subject-Out cross validation
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))
ts = np.squeeze(np.nonzero(ts))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
# Initializing output variables
validScores, testScores = [], []
trainLoss = np.zeros((len(fold_pairs), num_epochs))
validLoss = np.zeros((len(fold_pairs), num_epochs))
validEpochAccu = np.zeros((len(fold_pairs), num_epochs))
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum + 1,
len(fold_pairs)))
# Divide the dataset into train, validation and test sets
(X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(
data, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
# Normalizing the input
# trainMeans = [np.mean(X_train[:, i, :, :].flatten()) for i in range(X_train.shape[1])]
# trainStds = [np.std(X_train[:, i, :, :].flatten()) for i in range(X_train.shape[1])]
# for i in range(len(trainMeans)):
# X_train[:, i, :, :] = (X_train[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_val[:, i, :, :] = (X_val[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_test[:, i, :, :] = (X_test[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_train = (X_train - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_val = (X_val - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_test = (X_test - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_train = (X_train - np.mean(X_train, axis=0)) / np.float32(256)
# X_val = (X_val - np.mean(X_train, axis=0)) / np.float32(256)
# X_test = (X_test - np.mean(X_train, axis=0)) / np.float32(256)
# X_train = X_train / np.float32(256)
# X_val = X_val / np.float32(256)
# X_test = X_test / np.float32(256)
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
network = build_cnn(input_var)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(
prediction, target_var)
loss = loss.mean()
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = lasagne.layers.get_all_params(network, trainable=True)
# updates = lasagne.updates.nesterov_momentum(
# loss, params, learning_rate=0.001, momentum=0.9)
updates = lasagne.updates.adam(loss, params, learning_rate=0.001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network,
deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var],
loss,
updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var],
[test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
best_validation_accu = 0
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train,
y_train,
batch_size,
shuffle=False):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val,
y_val,
batch_size,
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
av_train_err = train_err / train_batches
av_val_err = val_err / val_batches
av_val_acc = val_acc / val_batches
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs,
time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(av_train_err))
print(" validation loss:\t\t{:.6f}".format(av_val_err))
print(" validation accuracy:\t\t{:.2f} %".format(av_val_acc *
100))
trainLoss[foldNum, epoch] = av_train_err
validLoss[foldNum, epoch] = av_val_err
validEpochAccu[foldNum, epoch] = av_val_acc * 100
if av_val_acc > best_validation_accu:
best_validation_accu = av_val_acc
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test,
y_test,
batch_size,
shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
av_test_err = test_err / test_batches
av_test_acc = test_acc / test_batches
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(av_test_err))
print(" test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
# Dump the network weights to a file like this:
np.savez('weights_lasg{0}'.format(foldNum),
*lasagne.layers.get_all_param_values(network))
validScores.append(best_validation_accu * 100)
testScores.append(av_test_acc * 100)
print('-' * 50)
print("Best validation accuracy:\t\t{:.2f} %".format(
best_validation_accu * 100))
print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
scipy.io.savemat(
'cnn_lasg_results_orig', {
'validAccu': validScores,
'testAccu': testScores,
'trainLoss': trainLoss,
'validLoss': validLoss,
'validEpochAccu': validEpochAccu
})
batch_size = 32
num_classes = len(np.unique(labels))
# CNN Mode
imsize = 32
# Find the average response over time windows
#av_feats = reduce(lambda x, y: x + y, [feats[:, i * 206:(i + 1) * 206] for i in range(feats.shape[1] / 206)])
#av_feats = av_feats / (feats.shape[1] / 206)
av_feats = reduce(
lambda x, y: x + y,
[feats[:, i * 206:(i + 1) * 206] for i in range(feats.shape[1] / 206)])
av_feats = av_feats / (feats.shape[1] / 206)
images = gen_images(np.array(locs_2d), av_feats, imsize, normalize=False)
(X_train,
y_train), (X_val, y_val), (X_test,
y_test) = reformatInput(images, labels, fold)
# (X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInputIdx(av_feats, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
#print('Generating training images...')
#X_train = gen_images(np.array(locs_2d), X_train, imsize, normalize=False)
#print('Genarating validation images...')
#X_val = gen_images(np.array(locs_2d), X_val, imsize, normalize=False)
#print('\n')
print('Building model...')
network = Sequential()
def main():
# Load the dataset
print("Loading data...")
data, labels = load_data(filename)
mat = scipy.io.loadmat(subjectsFilename, mat_dtype=True)
subjNumbers = np.squeeze(mat['subjectNum']) # subject IDs for each trial
# Create folds based on subject numbers (for leave-subject-out x-validation)
fold_pairs = []
# If augmentation is selected include augmented data
if augment:
# Aggregate augmented data and labels
data_aug, labels_aug = load_data(filename_aug)
data = np.vstack((data, data_aug))
labels = np.vstack((labels, labels_aug))
# Leave-Subject-Out cross validation
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts))) # Training indices
ts = np.squeeze(np.nonzero(ts))
# Include augmented training data
tr = np.concatenate((tr, tr+subjNumbers.size))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
else:
# Leave-Subject-Out cross validation
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))
ts = np.squeeze(np.nonzero(ts))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
# Initializing output variables
validScores, testScores = [], []
trainLoss = np.zeros((len(fold_pairs), num_epochs))
validLoss = np.zeros((len(fold_pairs), num_epochs))
validEpochAccu = np.zeros((len(fold_pairs), num_epochs))
for foldNum, fold in enumerate(fold_pairs):
print('Beginning fold {0} out of {1}'.format(foldNum+1, len(fold_pairs)))
# Divide the dataset into train, validation and test sets
(X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(data, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
# Normalizing the input
# trainMeans = [np.mean(X_train[:, i, :, :].flatten()) for i in range(X_train.shape[1])]
# trainStds = [np.std(X_train[:, i, :, :].flatten()) for i in range(X_train.shape[1])]
# for i in range(len(trainMeans)):
# X_train[:, i, :, :] = (X_train[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_val[:, i, :, :] = (X_val[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_test[:, i, :, :] = (X_test[:, i, :, :] - trainMeans[i]) / trainStds[i]
# X_train = (X_train - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_val = (X_val - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_test = (X_test - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_train = (X_train - np.mean(X_train, axis=0)) / np.float32(256)
# X_val = (X_val - np.mean(X_train, axis=0)) / np.float32(256)
# X_test = (X_test - np.mean(X_train, axis=0)) / np.float32(256)
# X_train = X_train / np.float32(256)
# X_val = X_val / np.float32(256)
# X_test = X_test / np.float32(256)
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
network = build_cnn(input_var)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize (for our multi-class problem, it is the cross-entropy loss):
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
# We could add some weight decay as well here, see lasagne.regularization.
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum, but Lasagne offers plenty more.
params = lasagne.layers.get_all_params(network, trainable=True)
# updates = lasagne.updates.nesterov_momentum(
# loss, params, learning_rate=0.001, momentum=0.9)
updates = lasagne.updates.adam(loss, params, learning_rate=0.001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function([input_var, target_var], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
# Finally, launch the training loop.
print("Starting training...")
best_validation_accu = 0
# We iterate over epochs:
for epoch in range(num_epochs):
# In each epoch, we do a full pass over the training data:
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train, y_train, batch_size, shuffle=False):
inputs, targets = batch
train_err += train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
for batch in iterate_minibatches(X_val, y_val, batch_size, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
av_train_err = train_err / train_batches
av_val_err = val_err / val_batches
av_val_acc = val_acc / val_batches
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(av_train_err))
print(" validation loss:\t\t{:.6f}".format(av_val_err))
print(" validation accuracy:\t\t{:.2f} %".format(av_val_acc * 100))
trainLoss[foldNum, epoch] = av_train_err
validLoss[foldNum, epoch] = av_val_err
validEpochAccu[foldNum, epoch] = av_val_acc * 100
if av_val_acc > best_validation_accu:
best_validation_accu = av_val_acc
# After training, we compute and print the test error:
test_err = 0
test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):
inputs, targets = batch
err, acc = val_fn(inputs, targets)
test_err += err
test_acc += acc
test_batches += 1
av_test_err = test_err / test_batches
av_test_acc = test_acc / test_batches
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(av_test_err))
print(" test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
# Dump the network weights to a file like this:
np.savez('weights_lasg{0}'.format(foldNum), *lasagne.layers.get_all_param_values(network))
validScores.append(best_validation_accu * 100)
testScores.append(av_test_acc * 100)
print('-'*50)
print("Best validation accuracy:\t\t{:.2f} %".format(best_validation_accu * 100))
print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
scipy.io.savemat('cnn_lasg_results_orig',
{'validAccu': validScores,
'testAccu': testScores,
'trainLoss': trainLoss,
'validLoss': validLoss,
'validEpochAccu': validEpochAccu
})
def main():
# the data, shuffled and split between tran and test sets
data, labels = load_data(filename)
mat = scipy.io.loadmat(subjectsFilename, mat_dtype=True)
subjNumbers = np.squeeze(mat['subjectNum']) # subject IDs for each trial
# Creating the folds
# kf = StratifiedKFold(np.squeeze(labels), n_folds=ksplit, shuffle=True, random_state=123)
# kf = KFold(labels.shape[0], n_folds=ksplit, shuffle=True, random_state=123)
# fold_pairs = [(tr, ts) for (tr, ts) in kf]
# Leave-Subject-Out cross validation
fold_pairs = []
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))
ts = np.squeeze(np.nonzero(ts))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
validScores, testScores = [], []
for foldNum, fold in enumerate(fold_pairs):
(X_train, y_train), (X_valid, y_valid), (X_test, y_test) = reformatInput(data, labels, fold)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_valid.shape[0], 'valid samples')
print(X_test.shape[0], 'test samples')
X_train = X_train.astype("float32", casting='unsafe')
X_valid = X_valid.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_valid = np_utils.to_categorical(y_valid, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
# Building the network
model = Sequential()
model.add(Convolution2D(40, 3, 3, border_mode='full',
input_shape=(image_dimensions, shapex, shapey)))
model.add(Activation('relu'))
model.add(Convolution2D(40, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# model.add(Convolution2D(80, 3, 3, border_mode='full'))
# model.add(Activation('relu'))
# model.add(Convolution2D(80, 3, 3))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# model.add(Convolution2D(nb_filters[0], image_dimensions, nb_conv[0], nb_conv[0], border_mode='full'))
# # model.add(BatchNormalization([nb_filters[0], nb_conv[0], nb_conv[0], image_dimensions]))
# model.add(Activation('relu'))
# model.add(Convolution2D(nb_filters[0], nb_filters[0], nb_conv[0], nb_conv[0]))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(poolsize=(nb_pool[0], nb_pool[0])))
# model.add(Dropout(0.25))
#
# model.add(Convolution2D(nb_filters[1], nb_filters[0], nb_conv[0], nb_conv[0], border_mode='full'))
# model.add(Activation('relu'))
# model.add(Convolution2D(nb_filters[1], nb_filters[1], nb_conv[1], nb_conv[1]))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(poolsize=(nb_pool[1], nb_pool[1])))
# model.add(Dropout(0.25))
#
# model.add(Flatten())
# # the image dimensions are the original dimensions divided by any pooling
# # each pixel has a number of filters, determined by the last Convolution2D layer
# model.add(Dense(nb_filters[-1] * (shapex / nb_pool[0] / nb_pool[1]) * (shapey / nb_pool[0] / nb_pool[1]), 1024))
# # model.add(BatchNormalization([1024]))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(1024, nb_classes))
# model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
if not data_augmentation:
print("Not using data augmentation or normalization")
X_train = X_train.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
X_train /= 255.
X_test /= 255.
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True)
score, accu = model.evaluate(X_test, Y_test, batch_size=batch_size, show_accuracy=True)
print('Test accuracy:', accu)
else:
print("Using real time data augmentation")
# X_train = (X_train - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_valid = (X_valid - np.mean(X_valid, axis=0)) / np.std(X_valid.flatten(), axis=0)
# X_test = (X_test - np.mean(X_test, axis=0)) / np.std(X_test.flatten(), axis=0)
# X_train = (X_train - np.mean(X_train, axis=0))
# X_valid = (X_valid - np.mean(X_train, axis=0))
# X_test = (X_test - np.mean(X_train, axis=0))
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=True, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0, # randomly shift images horizontally (fraction of total width)
height_shift_range=0, # randomly shift images vertically (fraction of total height)
horizontal_flip=False, # randomly flip images
vertical_flip=False) # randomly flip images
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(X_train)
best_validation_accu = 0
for e in range(nb_epoch):
print('-'*40)
print('Epoch', e)
print('-'*40)
print("Training...")
# batch train with realtime data augmentation
progbar = generic_utils.Progbar(X_train.shape[0])
for X_batch, Y_batch in datagen.flow(X_train, Y_train, batch_size=batch_size):
score, trainAccu = model.train_on_batch(X_batch, Y_batch, accuracy=True)
progbar.add(X_batch.shape[0], values=[("train accuracy", trainAccu)])
print("Validating...")
# Validation time!
progbar = generic_utils.Progbar(X_valid.shape[0])
epochValidAccu = []
for X_batch, Y_batch in datagen.flow(X_valid, Y_valid, batch_size=batch_size):
score, validAccu = model.test_on_batch(X_batch, Y_batch, accuracy=True)
epochValidAccu.append(validAccu)
progbar.add(X_batch.shape[0], values=[("validation accuracy", validAccu)])
meanValidAccu = np.mean(epochValidAccu)
if meanValidAccu > best_validation_accu:
best_validation_accu = meanValidAccu
best_iter = e
print("Testing...")
# test time!
progbar = generic_utils.Progbar(X_test.shape[0])
epochTestAccu = []
for X_batch, Y_batch in datagen.flow(X_test, Y_test, batch_size=batch_size):
score, testAccu = model.test_on_batch(X_batch, Y_batch, accuracy=True)
epochTestAccu.append(testAccu)
progbar.add(X_batch.shape[0], values=[("test accuracy", testAccu)])
model.save_weights('weigths_{0}'.format(foldNum), overwrite=True)
validScores.append(best_validation_accu)
testScores.append(np.mean(epochTestAccu))
scipy.io.savemat('cnn_results', {'validAccu': validScores, 'testAccu': testScores})
print ('Average valid accuracies: {0}'.format(np.mean(validScores)))
print ('Average test accuracies: {0}'.format(np.mean(testScores)))
def main():
# the data, shuffled and split between tran and test sets
data, labels = load_data(filename)
mat = scipy.io.loadmat(subjectsFilename, mat_dtype=True)
subjNumbers = np.squeeze(mat['subjectNum']) # subject IDs for each trial
# Creating the folds
# kf = StratifiedKFold(np.squeeze(labels), n_folds=ksplit, shuffle=True, random_state=123)
# kf = KFold(labels.shape[0], n_folds=ksplit, shuffle=True, random_state=123)
# fold_pairs = [(tr, ts) for (tr, ts) in kf]
# Leave-Subject-Out cross validation
fold_pairs = []
for i in np.unique(subjNumbers):
ts = subjNumbers == i
tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))
ts = np.squeeze(np.nonzero(ts))
np.random.shuffle(tr) # Shuffle indices
np.random.shuffle(ts)
fold_pairs.append((tr, ts))
validScores, testScores = [], []
for foldNum, fold in enumerate(fold_pairs):
(X_train,
y_train), (X_valid,
y_valid), (X_test,
y_test) = reformatInput(data, labels, fold)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_valid.shape[0], 'valid samples')
print(X_test.shape[0], 'test samples')
X_train = X_train.astype("float32", casting='unsafe')
X_valid = X_valid.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_valid = np_utils.to_categorical(y_valid, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
# Building the network
model = Sequential()
model.add(
Convolution2D(40,
3,
3,
border_mode='full',
input_shape=(image_dimensions, shapex, shapey)))
model.add(Activation('relu'))
model.add(Convolution2D(40, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# model.add(Convolution2D(80, 3, 3, border_mode='full'))
# model.add(Activation('relu'))
# model.add(Convolution2D(80, 3, 3))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# model.add(Convolution2D(nb_filters[0], image_dimensions, nb_conv[0], nb_conv[0], border_mode='full'))
# # model.add(BatchNormalization([nb_filters[0], nb_conv[0], nb_conv[0], image_dimensions]))
# model.add(Activation('relu'))
# model.add(Convolution2D(nb_filters[0], nb_filters[0], nb_conv[0], nb_conv[0]))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(poolsize=(nb_pool[0], nb_pool[0])))
# model.add(Dropout(0.25))
#
# model.add(Convolution2D(nb_filters[1], nb_filters[0], nb_conv[0], nb_conv[0], border_mode='full'))
# model.add(Activation('relu'))
# model.add(Convolution2D(nb_filters[1], nb_filters[1], nb_conv[1], nb_conv[1]))
# model.add(Activation('relu'))
# model.add(MaxPooling2D(poolsize=(nb_pool[1], nb_pool[1])))
# model.add(Dropout(0.25))
#
# model.add(Flatten())
# # the image dimensions are the original dimensions divided by any pooling
# # each pixel has a number of filters, determined by the last Convolution2D layer
# model.add(Dense(nb_filters[-1] * (shapex / nb_pool[0] / nb_pool[1]) * (shapey / nb_pool[0] / nb_pool[1]), 1024))
# # model.add(BatchNormalization([1024]))
# model.add(Activation('relu'))
# model.add(Dropout(0.5))
# model.add(Dense(1024, nb_classes))
# model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
if not data_augmentation:
print("Not using data augmentation or normalization")
X_train = X_train.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
X_train /= 255.
X_test /= 255.
model.fit(X_train,
Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
show_accuracy=True)
score, accu = model.evaluate(X_test,
Y_test,
batch_size=batch_size,
show_accuracy=True)
print('Test accuracy:', accu)
else:
print("Using real time data augmentation")
# X_train = (X_train - np.mean(X_train, axis=0)) / np.std(X_train.flatten(), axis=0)
# X_valid = (X_valid - np.mean(X_valid, axis=0)) / np.std(X_valid.flatten(), axis=0)
# X_test = (X_test - np.mean(X_test, axis=0)) / np.std(X_test.flatten(), axis=0)
# X_train = (X_train - np.mean(X_train, axis=0))
# X_valid = (X_valid - np.mean(X_train, axis=0))
# X_test = (X_test - np.mean(X_train, axis=0))
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=True, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=
False, # divide inputs by std of the dataset
samplewise_std_normalization=
False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=
0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=
0, # randomly shift images horizontally (fraction of total width)
height_shift_range=
0, # randomly shift images vertically (fraction of total height)
horizontal_flip=False, # randomly flip images
vertical_flip=False) # randomly flip images
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(X_train)
best_validation_accu = 0
for e in range(nb_epoch):
print('-' * 40)
print('Epoch', e)
print('-' * 40)
print("Training...")
# batch train with realtime data augmentation
progbar = generic_utils.Progbar(X_train.shape[0])
for X_batch, Y_batch in datagen.flow(X_train,
Y_train,
batch_size=batch_size):
score, trainAccu = model.train_on_batch(X_batch,
Y_batch,
accuracy=True)
progbar.add(X_batch.shape[0],
values=[("train accuracy", trainAccu)])
print("Validating...")
# Validation time!
progbar = generic_utils.Progbar(X_valid.shape[0])
epochValidAccu = []
for X_batch, Y_batch in datagen.flow(X_valid,
Y_valid,
batch_size=batch_size):
score, validAccu = model.test_on_batch(X_batch,
Y_batch,
accuracy=True)
epochValidAccu.append(validAccu)
progbar.add(X_batch.shape[0],
values=[("validation accuracy", validAccu)])
meanValidAccu = np.mean(epochValidAccu)
if meanValidAccu > best_validation_accu:
best_validation_accu = meanValidAccu
best_iter = e
print("Testing...")
# test time!
progbar = generic_utils.Progbar(X_test.shape[0])
epochTestAccu = []
for X_batch, Y_batch in datagen.flow(
X_test, Y_test, batch_size=batch_size):
score, testAccu = model.test_on_batch(X_batch,
Y_batch,
accuracy=True)
epochTestAccu.append(testAccu)
progbar.add(X_batch.shape[0],
values=[("test accuracy", testAccu)])
model.save_weights('weigths_{0}'.format(foldNum),
overwrite=True)
validScores.append(best_validation_accu)
testScores.append(np.mean(epochTestAccu))
scipy.io.savemat('cnn_results', {
'validAccu': validScores,
'testAccu': testScores
})
print('Average valid accuracies: {0}'.format(np.mean(validScores)))
print('Average test accuracies: {0}'.format(np.mean(testScores)))
def train(images, labels, fold, model_type, batch_size=32, num_epochs=5, n_layer = (4, 2, 1)):
print('model type:', model_type)
#for data parsing
num_classes = 4
if len(images.shape) == 4:
sampleN, images_height, images_width, colorN = images.shape
print('4:', images.shape)
input_var = tf.placeholder(tf.float32, [None, images_height, images_width, colorN])
output_var = tf.placeholder(tf.float32, [None, num_classes])
elif len(images.shape) == 5:
windowN, sampleN, images_height, images_width, colorN = images.shape
#print('5:', images.shape)
#images = np.swapaxes(images, 0, 1)
print('5:', images.shape)
input_var = tf.placeholder(tf.float32, [None, windowN, images_height, images_width, colorN])
output_var = tf.placeholder(tf.float32, [None, num_classes])
else:
print('warning!')
return False
#data devide
(X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(images, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
keep_prob = tf.placeholder(tf.float32)
#accordance to ... model.
if model_type == '1dconv':
#network = build_convpool_conv1d(input_var, num_classes, n_timewin = windowN)
network = build_convpool_conv1d(input_var, num_classes)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=output_var))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
elif model_type == 'maxpool':
network = build_convpool_max(input_var, num_classes)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=output_var))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
elif model_type == 'mix':
network = build_convpool_mix(input_var, num_classes, 100)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=output_var))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
elif model_type == 'lstm':
network = build_convpool_lstm(input_var, num_classes, 100)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=output_var))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
elif model_type == 'cnn':
network, _ = build_cnn(input_var, n_layers = n_layer)
#define fully connected network
network = tf.reshape(network, [-1, 4*4*128])
network = tf.add(tf.matmul(network, weight_variable([4*4*128, 512])), bias_variable([512]))
network = tf.nn.relu(network)
network = tf.nn.dropout(network, 0.5)
#define out layer
network = tf.add(tf.matmul(network, weight_variable([512, num_classes])),bias_variable([num_classes]))
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=network, labels=output_var))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
else:
raise ValueError("Model not supported ['1dconv', 'maxpool', 'lstm', 'mix', 'cnn']")
#for monitor final network
print("here is our network shape")
print(network.shape)
print(output_var.shape)
#create a loss expression for training, i.e. ascalar objective we want
#to minimize (for our multi-class problem, .... o
#evaluate model
correct_pred = tf.equal(tf.argmax(network, 1), tf.argmax(output_var, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
#MODEL Def end
#Traingning & varlidation..
print("start training")
# Initializing the variables
init = tf.global_variables_initializer()
display_step = 1
xin = input_var
yout = output_var
#for saving weight
saver = tf.train.Saver()
filepath = "./tmp/"
filesave_path = filepath + model_type + ".ckpt"
# gpu config
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#run session
with tf.Session(config = config) as sess:
writer = tf.summary.FileWriter('logs', sess.graph)
sess.run(init)
best_val_acc = 0
for epoch in range(num_epochs):
train_loss = 0
val_loss = 0
train_batches = 0
start_time = time.time()
train_step = 1
val_step = 1
val_batches = 0
sum_train_acc = 0
sum_train_loss = 0
sum_val_acc = 0
sum_val_loss = 0
# train accuracy
for batch in iterate_minibatches(X_train, y_train, batch_size, shuffle=False):
inputs, targets = batch
targets = np.eye(num_classes)[targets.reshape(-1)]
#check for monitor size
if train_step ==1:
print("batch info is.. batch, inputs.shape, targets.shape")
print(X_train.shape)
print(y_train.shape)
print("batch info")
print(inputs.shape)
print(targets.shape)
print(xin.shape)
print(yout.shape)
sess.run(optimizer, feed_dict = {xin: inputs, yout: targets, keep_prob: 0.5})
if train_step % display_step == 0:
#calculate batch loss & accuracy
train_loss, train_acc = sess.run([cost, accuracy], feed_dict = {xin: inputs, yout: targets, keep_prob: 1.0})
print("Iter " + str(train_step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(train_loss) + ", Training Accuracy= " + \
"{:.5f}".format(train_acc))
sum_train_loss += train_loss
sum_train_acc += train_acc
print(train_loss, sum_train_loss, train_acc, sum_train_acc)
save_path = saver.save(sess, filesave_path, global_step = train_step)
train_step += 1
print("train Opt fin")
# validation accuracy
for batch in iterate_minibatches(X_val, y_val, batch_size, shuffle=False):
inputs, targets = batch
targets = np.eye(num_classes)[targets.reshape(-1)]
sess.run(optimizer, feed_dict = {xin: inputs, yout: targets, keep_prob: 0.5})
if val_step % display_step == 0:
#calculate batch loss & accuracy
val_acc = sess.run(accuracy, feed_dict = {xin: inputs, yout: targets, keep_prob: 1.0})
print("Iter " + str(val_step*batch_size) + ", Minibatch Loss= " + \
", cross validatino Accuracy= " + "{:.5f}".format(val_acc))
#sum_val_loss += val_loss
sum_val_acc += val_acc
print(val_acc, sum_val_acc)
val_step += 1
print("Validation Opt fin")
#calculate average loss & accuracy
av_train_loss = sum_train_loss/train_step*display_step
av_train_acc = sum_train_acc/train_step*display_step
#av_val_loss = sum_val_loss/val_step*display_step
av_val_acc = sum_val_acc/val_step*display_step
print(av_train_loss, av_train_acc, av_val_acc)
# Then we print the results for this epoch:
print("Epoch {} of {} took {:.3f}s".format(epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(av_train_loss))
#print(" validation loss:\t\t{:.6f}".format(av_val_loss))
print(" validation accuracy:\t\t{:.2f} %".format(av_val_acc * 100))
if av_val_acc > best_val_acc:
best_val_acc = av_val_acc
# After training, we compute & print the test error
sum_test_err = 0
sum_test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):
inputs, targets = batch
targets = np.eye(num_classes)[targets.reshape(-1)]
test_acc = sess.run(accuracy, feed_dict={xin: inputs, yout: targets, keep_prob: 1.0})
print(test_acc)
sum_test_acc += test_acc
test_batches += 1
av_test_acc = sum_test_acc / test_batches
print("Final results:")
#print(" test loss:\t\t\t{:.6f}".format(av_test_err))
print(" test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
# Dump the network weights to a file like this:
#tf.saver.save(sess, 'my-model')
# `save` method will call `export_meta_graph` implicitly.
# you will get saved graph files:my-model.meta
print('-'*50)
print("Best validation accuracy:\t\t{:.2f} %".format(best_val_acc * 100))
print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
writer = tf.summary.FileWriter('logs', sess.graph)
#modelsave part
save_path = saver.save(sess, filesave_path)
print("Model saved in file: %s" % save_path)
batch_size = 32
num_epochs = 5
num_classes = len(np.unique(labels))
# CNN Mode
imsize = 32
# Find the average response over time windows
av_feats = reduce(lambda x, y: x + y, [feats[:, i * 206:(i + 1) * 206] for i in range(feats.shape[1] / 206)])
av_feats = av_feats / (feats.shape[1] / 206)
images = gen_images(np.array(locs_2d), av_feats, imsize, normalize=False)
# images = np.load('images.npy')
np.save('images', images)
(X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(images, labels, fold)
X_train = X_train.astype("float32", casting='unsafe')
X_val = X_val.astype("float32", casting='unsafe')
X_test = X_test.astype("float32", casting='unsafe')
print('Building model...')
network = Sequential()
network.add(InputLayer(input_shape=(3, imsize, imsize)))
network.add(Conv2D(32, 3, padding='same', data_format='channels_first', activation='relu'))
network.add(Conv2D(32, 3, padding='same', data_format='channels_first', activation='relu'))
network.add(Conv2D(32, 3, padding='same', data_format='channels_first', activation='relu'))
network.add(Conv2D(32, 3, padding='same', data_format='channels_first', activation='relu'))