def _make_args(self, X, Z, imp_weight=None): from breze.learn.base import cast_array_to_local_type batch_size = getattr(self, "batch_size", None) if batch_size is None: X, Z = cast_array_to_local_type(X), cast_array_to_local_type(Z) if imp_weight is not None: imp_weight = cast_array_to_local_type(imp_weight) data = itertools.repeat([X, Z, imp_weight]) else: data = itertools.repeat([X, Z]) elif batch_size < 1: raise ValueError("need strictly positive batch size") else: if imp_weight is not None: data = self.iter_minibatches( [self.dim_max, self.Z, imp_weight], self.batch_size, list(self.sample_dim) + [self.sample_dim[0]] ) data = ( (cast_array_to_local_type(x), cast_array_to_local_type(z), cast_array_to_local_type(w)) for x, z, w in data ) else: data = self.iter_minibatches([self.X, self.Z], self.batch_size, self.sample_dim) # data = self.iter_minibatches([self.dim_max, self.Z], self.batch_size,self.sample_dim) data = ((cast_array_to_local_type(x), cast_array_to_local_type(z)) for x, z in data) args = ((i, {}) for i in data) return args
def _make_args(self, X, Z, imp_weight=None): from breze.learn.base import cast_array_to_local_type batch_size = getattr(self, 'batch_size', None) if batch_size is None: X, Z = cast_array_to_local_type(X), cast_array_to_local_type(Z) if imp_weight is not None: imp_weight = cast_array_to_local_type(imp_weight) data = itertools.repeat([X, Z, imp_weight]) else: data = itertools.repeat([X, Z]) elif batch_size < 1: raise ValueError('need strictly positive batch size') else: if imp_weight is not None: data = self.iter_minibatches( [self.dim_max, self.Z, imp_weight], self.batch_size, list(self.sample_dim) + [self.sample_dim[0]]) data = ((cast_array_to_local_type(x), cast_array_to_local_type(z), cast_array_to_local_type(w)) for x, z, w in data) else: data = self.iter_minibatches([self.X, self.Z], self.batch_size, self.sample_dim) #data = self.iter_minibatches([self.dim_max, self.Z], self.batch_size,self.sample_dim) data = ((cast_array_to_local_type(x), cast_array_to_local_type(z)) for x, z in data) args = ((i, {}) for i in data) return args
# Load data. with gzip.open(datafile,'rb') as f: train_set, val_set, test_set = cPickle.load(f) X, Z = train_set VX, VZ = val_set TX, TZ = test_set Z = one_hot(Z, 10) VZ = one_hot(VZ, 10) TZ = one_hot(TZ, 10) image_dims = 28, 28 X, Z, VX, VZ, TX, TZ = [cast_array_to_local_type(i) for i in (X, Z, VX,VZ, TX, TZ)] batch_size = 100 #optimizer = 'rmsprop', {'step_rate': 1e-4, 'momentum': 0.95, 'decay': .95, 'offset': 1e-6} #optimizer = 'adam', {'step_rate': .5, 'momentum': 0.9, 'decay': .95, 'offset': 1e-6} optimizer = 'gd' fast_dropout = True if fast_dropout: class MyVAE(mlp.FastDropoutVariationalAutoEncoder, mlp.FastDropoutMlpGaussLatentVAEMixin, mlp.FastDropoutMlpBernoulliVisibleVAEMixin):
with gzip.open(datafile, 'rb') as f: train_set, val_set, test_set = cPickle.load(f) X, Z = train_set VX, VZ = val_set TX, TZ = test_set Z = one_hot(Z, 10) VZ = one_hot(VZ, 10) TZ = one_hot(TZ, 10) image_dims = 28, 28 X, Z, VX, VZ, TX, TZ = [ cast_array_to_local_type(i) for i in (X, Z, VX, VZ, TX, TZ) ] batch_size = 100 #optimizer = 'rmsprop', {'step_rate': 1e-4, 'momentum': 0.95, 'decay': .95, 'offset': 1e-6} #optimizer = 'adam', {'step_rate': .5, 'momentum': 0.9, 'decay': .95, 'offset': 1e-6} optimizer = 'gd' fast_dropout = True if fast_dropout: class MyVAE(mlp.FastDropoutVariationalAutoEncoder, mlp.FastDropoutMlpGaussLatentVAEMixin, mlp.FastDropoutMlpBernoulliVisibleVAEMixin): pass