# Iterate over batch start index
for batch_idx in range(0, len(Y_files), batch_size):
# Store the npy files corresponding to this batch
npy_mapping += Y_files[batch_idx:batch_idx + batch_size]
# Load in a list of this batch
batch = [np.load(Y_files[n])
for n in range(batch_idx, batch_idx + batch_size)
if n < len(Y_files)]
# Compute mean/std for each sequence in this batch
for n, Y in enumerate(batch):
statistics_Y[batch_idx + n, :D] = np.mean(Y, axis=0)
statistics_Y[batch_idx + n, D:] = np.std(Y, axis=0)
# Normalize batch by trianing set mean/std
batch = [(Y - Y_mean)/Y_std for Y in batch]
# Randomly sample subsequences of this batch
batch = utils.sample_sequences(batch, max_length_Y)
# Embed the batch and store the embedding in the matrix
embedded_Y[batch_idx:batch_idx + batch_size] = embed_Y(batch)
# Report percent done and time
print "\r{:.3f}% in {}s".format(
(100.*(batch_idx + batch_size))/len(Y_files),
time.time() - now),
now = time.time()
sys.stdout.flush()
print 'Done.'
# Write out embedding matrix statistics matrix and mapping
np.save('data/msd_embedded.npy', embedded_Y)
np.save('data/msd_statistics.npy', statistics_Y)
with open('data/msd_embedded_mapping.pkl', 'wb') as f:
pickle.dump(npy_mapping, f)
def train(data, sample_size_X, sample_size_Y, conv_layer_specs,
dense_layer_specs, alpha_XY, m_XY, optimizer=lasagne.updates.rmsprop,
batch_size=20, epoch_size=100, initial_patience=1000,
improvement_threshold=0.99, patience_increase=5, max_iter=100000):
''' Utility function for training a siamese net for cross-modality hashing
Assumes data['X_train'][n] should be mapped close to data['Y_train'][m]
only when n == m
:parameters:
- data : dict of list of np.ndarray
Training/validation sequences in X/Y modality
Should contain keys X_train, Y_train, X_validate, Y_validate
Sequence matrix shape=(n_sequences, n_time_steps, n_features)
- sample_size_X, sample_size_Y : int
Sampled sequence length for X/Y modalities
- conv_layer_specs, dense_layer_specs : list of dict
List of dicts, where each dict corresponds to keyword arguments
for each subsequent layer. Note that
dense_layer_specs[-1]['num_units'] should be the output
dimensionality of the network.
- alpha_XY : float
Scaling parameter for cross-modality negative example cost
- m_XY : int
Cross-modality negative example threshold
- optimizer: function
Function which takes a Theano expression and parameters and
computes parameter updates to minimize the Theano expression (for
example, something from lasagne.updates).
- batch_size : int
Mini-batch size
- epoch_size : int
Number of mini-batches per epoch
- initial_patience : int
Always train on at least this many batches
- improvement_threshold : float
Validation cost must decrease by this factor to increase patience
- patience_increase : int
How many more epochs should we wait when we increase patience
- max_iter : int
Maximum number of batches to train on
:returns:
- epoch : iterator
Results for each epoch are yielded
'''
# Create networks
layers = {
'X': utils.build_network(
(None, None, data['X_train'][0].shape[-1]), conv_layer_specs,
dense_layer_specs),
'Y': utils.build_network(
(None, None, data['Y_train'][0].shape[-1]), conv_layer_specs,
dense_layer_specs)}
# Inputs to X modality neural nets
X_p_input = T.tensor3('X_p_input')
X_n_input = T.tensor3('X_n_input')
# Y network
Y_p_input = T.tensor3('Y_p_input')
Y_n_input = T.tensor3('Y_n_input')
# Compute \sum max(0, m - ||a - b||_2)^2
def hinge_cost(m, a, b):
dist = m - T.sqrt(T.sum((a - b)**2, axis=1))
return T.mean((dist*(dist > 0))**2)
def hasher_cost(deterministic):
X_p_output = lasagne.layers.get_output(
layers['X']['out'],
{layers['X']['in']: X_p_input},
deterministic=deterministic)
X_n_output = lasagne.layers.get_output(
layers['X']['out'],
{layers['X']['in']: X_n_input},
deterministic=deterministic)
Y_p_output = lasagne.layers.get_output(
layers['Y']['out'],
{layers['Y']['in']: Y_p_input},
deterministic=deterministic)
Y_n_output = lasagne.layers.get_output(
layers['Y']['out'],
{layers['Y']['in']: Y_n_input},
deterministic=deterministic)
# Unthresholded, unscaled cost of positive examples across modalities
cost_p = T.mean(T.sum((X_p_output - Y_p_output)**2, axis=1))
# Thresholded, scaled cost of cross-modality negative examples
cost_n = alpha_XY*hinge_cost(m_XY, X_n_output, Y_n_output)
# Sum positive and negative costs for overall cost
cost = cost_p + cost_n
return cost
# Combine all parameters from both networks
params = (lasagne.layers.get_all_params(layers['X']['out'])
+ lasagne.layers.get_all_params(layers['Y']['out']))
# Compute RMSProp gradient descent updates
updates = optimizer(hasher_cost(False), params)
# Function for training the network
train = theano.function([X_p_input, X_n_input, Y_p_input, Y_n_input],
hasher_cost(False), updates=updates)
# Compute cost without training
cost = theano.function([X_p_input, X_n_input, Y_p_input, Y_n_input],
hasher_cost(True))
# Compute output without training
X_output = theano.function(
[layers['X']['in'].input_var],
lasagne.layers.get_output(layers['X']['out'], deterministic=True))
Y_output = theano.function(
[layers['Y']['in'].input_var],
lasagne.layers.get_output(layers['Y']['out'], deterministic=True))
# Start with infinite validate cost; we will always increase patience once
current_validate_cost = np.inf
patience = initial_patience
# Create sampled sequences for validation
X_validate = utils.sample_sequences(
data['X_validate'], sample_size_X)
Y_validate = utils.sample_sequences(
data['Y_validate'], sample_size_Y)
# Create fixed negative example validation set
X_validate_shuffle = np.random.permutation(X_validate.shape[0])
Y_validate_shuffle = X_validate_shuffle[
utils.random_derangement(X_validate.shape[0])]
# Create iterator to sample sequences from training data
data_iterator = utils.get_next_batch(
data['X_train'], data['Y_train'], sample_size_X, sample_size_Y,
batch_size, max_iter)
# We will accumulate the mean train cost over each epoch
train_cost = 0
for n, (X_p, Y_p, X_n, Y_n) in enumerate(data_iterator):
# Occasionally Theano was raising a MemoryError, this fails gracefully
try:
train_cost += train(X_p, X_n, Y_p, Y_n)
except MemoryError as e:
print "MemoryError: {}".format(e)
return
# Stop training if a NaN is encountered
if not np.isfinite(train_cost):
print 'Bad training cost {} at iteration {}'.format(train_cost, n)
break
# Validate the net after each epoch
if n and (not n % epoch_size):
epoch_result = collections.OrderedDict()
epoch_result['iteration'] = n
# Compute average training cost over the epoch
epoch_result['train_cost'] = train_cost / float(epoch_size)
# Reset training cost mean accumulation
train_cost = 0
# We need to accumulate the validation cost and network output over
# batches to avoid MemoryErrors
epoch_result['validate_cost'] = 0
validate_batches = 0
X_val_output = []
Y_val_output = []
for batch_idx in range(0, X_validate.shape[0], batch_size):
# Extract slice from validation set for this batch
batch_slice = slice(batch_idx, batch_idx + batch_size)
# Compute and accumulate cost
epoch_result['validate_cost'] += cost(
X_validate[batch_slice],
X_validate[X_validate_shuffle][batch_slice],
Y_validate[batch_slice],
Y_validate[Y_validate_shuffle][batch_slice])
# Keep track of # of batches for normalization
validate_batches += 1
# Compute network output and accumulate result
X_val_output.append(X_output(X_validate[batch_slice]))
Y_val_output.append(Y_output(Y_validate[batch_slice]))
# Normalize cost by number of batches and store
epoch_result['validate_cost'] /= float(validate_batches)
# Concatenate per-batch output to tensors
X_val_output = np.concatenate(X_val_output, axis=0)
Y_val_output = np.concatenate(Y_val_output, axis=0)
# Compute in-class and out-of-class distances
in_dists = np.mean((X_val_output - Y_val_output)**2, axis=1)
out_dists = np.mean((X_val_output[X_validate_shuffle] -
Y_val_output[Y_validate_shuffle])**2, axis=1)
# Objective is Bhattacharrya coefficient of in-class and
# out-of-class distances
epoch_result['validate_objective'] = utils.bhatt_coeff(
in_dists, out_dists)
# Test whether this validate cost is the new smallest
if epoch_result['validate_cost'] < current_validate_cost:
# To update patience, we must be smaller than
# improvement_threshold*(previous lowest validation cost)
patience_cost = improvement_threshold*current_validate_cost
if epoch_result['validate_cost'] < patience_cost:
# Increase patience by the supplied about
patience += epoch_size*patience_increase
# Even if we didn't increase patience, update lowest valid cost
current_validate_cost = epoch_result['validate_cost']
# Store patience after this epoch
epoch_result['patience'] = patience
# Yield scores and statistics for this epoch
X_params = lasagne.layers.get_all_param_values(layers['X']['out'])
Y_params = lasagne.layers.get_all_param_values(layers['Y']['out'])
yield (epoch_result, X_params, Y_params)
if n > patience:
break
return