def func(data, n_batch=0, randomorder=True, data_global={}):
data = ndict.ordered(ndict.flatten(data))
data_global = ndict.ordered(ndict.flatten(data_global))
# Check if keys of 'x' and 'inputs' match
allkeys = (data.keys() + data_global.keys())
for i in range(len(data)):
if x.keys()[i] not in allkeys:
raise Exception('Non-matching keys:'+str(allkeys)+' vs. '+str(x.keys()))
# Compile function if not already done
if f[0] == None:
_compile()
if n_batch <= 0:
# Get results
_data = data.copy()
_data.update(data_global)
inputs_ordered = ndict.orderedvals((_data,))
_result = f[0](*inputs_ordered)
# Put it in a dictionary with the corresponding keys
result = {y.keys()[i]: _result[i] for i in range(len(y))}
else:
# Minibatch-based evaluation.
# This assumes that input and output are tensors, and the first dimension iterates of datapoints
n_tot = data.itervalues().next().shape[0]
n_minibatches = int(math.ceil(1. * n_tot / n_batch))
n_tile = 1
if n_batch > n_tot:
assert n_batch%n_tot == 0
n_tile = n_batch/n_tot
indices = np.tile(np.arange(n_tot),n_tile)
if randomorder:
np.random.shuffle(indices)
adict = dict(zip(np.tile(np.arange(n_tot),n_tile),indices))
indices_inverse = sorted(adict, key=adict.get)
results = []
for i in range(n_minibatches):
data_minibatch = ndict.getRowsFromIndices(data, indices[i*n_batch:(i+1)*n_batch])
data_minibatch.update(data_global)
inputs_ordered = ndict.orderedvals((data_minibatch,))
results.append(f[0](*inputs_ordered))
if _debug:
print 'Function debug', i, results[-1]
if checknan == 'raise':
if np.isnan(np.sum(results[-1])):
print results[-1]
raise Exception("NaN detected")
result = {y.keys()[i]: np.concatenate([results[j][i] for j in range(n_minibatches)]) for i in range(len(y))}
if randomorder:
result = ndict.getRowsFromIndices(result, indices_inverse)
result = OrderedDict(sorted(result.items()))
# Return result
#raise Exception()
if return_single_y:
return result[result.keys()[0]]
return result
def func(data, n_batch=0, randomorder=True, data_global={}):
data = ndict.ordered(ndict.flatten(data))
data_global = ndict.ordered(ndict.flatten(data_global))
# Check if keys of 'x' and 'inputs' match
allkeys = (data.keys() + data_global.keys())
for i in range(len(data)):
if x.keys()[i] not in allkeys:
raise Exception('Non-matching keys:' + str(allkeys) + ' vs. ' +
str(x.keys()))
# Compile function if not already done
if f[0] == None:
_compile()
if n_batch <= 0:
# Get results
_data = data.copy()
_data.update(data_global)
inputs_ordered = ndict.orderedvals((_data, ))
_result = f[0](*inputs_ordered)
# Put it in a dictionary with the corresponding keys
result = {y.keys()[i]: _result[i] for i in range(len(y))}
else:
# Minibatch-based evaluation.
# This assumes that input and output are tensors, and the first dimension iterates of datapoints
n_tot = data.itervalues().next().shape[0]
n_minibatches = int(math.ceil(1. * n_tot / n_batch))
n_tile = 1
if n_batch > n_tot:
assert n_batch % n_tot == 0
n_tile = n_batch / n_tot
indices = np.tile(np.arange(n_tot), n_tile)
if randomorder:
np.random.shuffle(indices)
adict = dict(zip(np.tile(np.arange(n_tot), n_tile), indices))
indices_inverse = sorted(adict, key=adict.get)
results = []
for i in range(n_minibatches):
data_minibatch = ndict.getRowsFromIndices(
data, indices[i * n_batch:(i + 1) * n_batch])
data_minibatch.update(data_global)
inputs_ordered = ndict.orderedvals((data_minibatch, ))
results.append(f[0](*inputs_ordered))
if _debug:
print 'Function debug', i, results[-1]
if checknan == 'raise':
if np.isnan(np.sum(results[-1])):
print results[-1]
raise Exception("NaN detected")
result = {
y.keys()[i]:
np.concatenate([results[j][i] for j in range(n_minibatches)])
for i in range(len(y))
}
if randomorder:
result = ndict.getRowsFromIndices(result, indices_inverse)
result = OrderedDict(sorted(result.items()))
# Return result
#raise Exception()
if return_single_y:
return result[result.keys()[0]]
return result
def function(x, y, lazy=False, _debug=False, checknan='raise', **kwargs):
# Default keyword arguments
if not kwargs.has_key('on_unused_input'):
kwargs['on_unused_input'] = 'warn'
if not kwargs.has_key('mode'):
kwargs['mode'] = default_function_mode
# Order the input dict
x = ndict.ordered(ndict.flatten(x))
# Check the output dict
return_single_y = False
if not isinstance(y, dict):
return_single_y = True
y = {str(y): y}
y = ndict.ordered(y)
# Lazily compiled function (saves a lot of time)
f = [None]
def _compile(verbose=True):
t0 = time.time()
print 'Compiling... ',
#print '[graphy] Compiling function '+str(x.keys())+' => '+str(y.keys())+' ...'
sys.stdout.flush()
f[0] = theano.function(x.values(), y.values(), **kwargs)
print "%.2f" % (time.time() - t0), 's'
if not lazy:
_compile()
# The function to be called
def func(data, n_batch=0, randomorder=True, data_global={}):
data = ndict.ordered(ndict.flatten(data))
data_global = ndict.ordered(ndict.flatten(data_global))
# Check if keys of 'x' and 'inputs' match
allkeys = (data.keys() + data_global.keys())
for i in range(len(data)):
if x.keys()[i] not in allkeys:
raise Exception('Non-matching keys:' + str(allkeys) + ' vs. ' +
str(x.keys()))
# Compile function if not already done
if f[0] == None:
_compile()
if n_batch <= 0:
# Get results
_data = data.copy()
_data.update(data_global)
inputs_ordered = ndict.orderedvals((_data, ))
_result = f[0](*inputs_ordered)
# Put it in a dictionary with the corresponding keys
result = {y.keys()[i]: _result[i] for i in range(len(y))}
else:
# Minibatch-based evaluation.
# This assumes that input and output are tensors, and the first dimension iterates of datapoints
n_tot = data.itervalues().next().shape[0]
n_minibatches = int(math.ceil(1. * n_tot / n_batch))
n_tile = 1
if n_batch > n_tot:
assert n_batch % n_tot == 0
n_tile = n_batch / n_tot
indices = np.tile(np.arange(n_tot), n_tile)
if randomorder:
np.random.shuffle(indices)
adict = dict(zip(np.tile(np.arange(n_tot), n_tile), indices))
indices_inverse = sorted(adict, key=adict.get)
results = []
for i in range(n_minibatches):
data_minibatch = ndict.getRowsFromIndices(
data, indices[i * n_batch:(i + 1) * n_batch])
data_minibatch.update(data_global)
inputs_ordered = ndict.orderedvals((data_minibatch, ))
results.append(f[0](*inputs_ordered))
if _debug:
print 'Function debug', i, results[-1]
if checknan == 'raise':
if np.isnan(np.sum(results[-1])):
print results[-1]
raise Exception("NaN detected")
result = {
y.keys()[i]:
np.concatenate([results[j][i] for j in range(n_minibatches)])
for i in range(len(y))
}
if randomorder:
result = ndict.getRowsFromIndices(result, indices_inverse)
result = OrderedDict(sorted(result.items()))
# Return result
#raise Exception()
if return_single_y:
return result[result.keys()[0]]
return result
# Return the func
return G.Struct(__call__=func, f=f)
def function(x, y, lazy=False, _debug=False, checknan='raise', **kwargs):
# Default keyword arguments
if not kwargs.has_key('on_unused_input'):
kwargs['on_unused_input'] = 'warn'
if not kwargs.has_key('mode'):
kwargs['mode'] = default_function_mode
# Order the input dict
x = ndict.ordered(ndict.flatten(x))
# Check the output dict
return_single_y = False
if not isinstance(y, dict):
return_single_y = True
y = {str(y): y}
y = ndict.ordered(y)
# Lazily compiled function (saves a lot of time)
f = [None]
def _compile(verbose=True):
t0 = time.time()
print 'Compiling... ',
#print '[graphy] Compiling function '+str(x.keys())+' => '+str(y.keys())+' ...'
sys.stdout.flush()
f[0] = theano.function(x.values(), y.values(), **kwargs)
print "%.2f" % (time.time()-t0), 's'
if not lazy:
_compile()
# The function to be called
def func(data, n_batch=0, randomorder=True, data_global={}):
data = ndict.ordered(ndict.flatten(data))
data_global = ndict.ordered(ndict.flatten(data_global))
# Check if keys of 'x' and 'inputs' match
allkeys = (data.keys() + data_global.keys())
for i in range(len(data)):
if x.keys()[i] not in allkeys:
raise Exception('Non-matching keys:'+str(allkeys)+' vs. '+str(x.keys()))
# Compile function if not already done
if f[0] == None:
_compile()
if n_batch <= 0:
# Get results
_data = data.copy()
_data.update(data_global)
inputs_ordered = ndict.orderedvals((_data,))
_result = f[0](*inputs_ordered)
# Put it in a dictionary with the corresponding keys
result = {y.keys()[i]: _result[i] for i in range(len(y))}
else:
# Minibatch-based evaluation.
# This assumes that input and output are tensors, and the first dimension iterates of datapoints
n_tot = data.itervalues().next().shape[0]
n_minibatches = int(math.ceil(1. * n_tot / n_batch))
n_tile = 1
if n_batch > n_tot:
assert n_batch%n_tot == 0
n_tile = n_batch/n_tot
indices = np.tile(np.arange(n_tot),n_tile)
if randomorder:
np.random.shuffle(indices)
adict = dict(zip(np.tile(np.arange(n_tot),n_tile),indices))
indices_inverse = sorted(adict, key=adict.get)
results = []
for i in range(n_minibatches):
data_minibatch = ndict.getRowsFromIndices(data, indices[i*n_batch:(i+1)*n_batch])
data_minibatch.update(data_global)
inputs_ordered = ndict.orderedvals((data_minibatch,))
results.append(f[0](*inputs_ordered))
if _debug:
print 'Function debug', i, results[-1]
if checknan == 'raise':
if np.isnan(np.sum(results[-1])):
print results[-1]
raise Exception("NaN detected")
result = {y.keys()[i]: np.concatenate([results[j][i] for j in range(n_minibatches)]) for i in range(len(y))}
if randomorder:
result = ndict.getRowsFromIndices(result, indices_inverse)
result = OrderedDict(sorted(result.items()))
# Return result
#raise Exception()
if return_single_y:
return result[result.keys()[0]]
return result
# Return the func
return G.Struct(__call__=func, f=f)