from collections import OrderedDict import numpy as np import theano from dagbldr.datasets import fetch_mnist, minibatch_iterator from dagbldr.optimizers import rmsprop from dagbldr.utils import add_datasets_to_graph, get_params_and_grads from dagbldr.utils import get_weights_from_graph from dagbldr.utils import convert_to_one_hot from dagbldr.utils import TrainingLoop from dagbldr.utils import create_checkpoint_dict from dagbldr.nodes import relu_layer, softmax_zeros_layer from dagbldr.nodes import categorical_crossentropy mnist = fetch_mnist() train_indices = mnist["train_indices"] valid_indices = mnist["valid_indices"] X = mnist["data"] y = mnist["target"] n_targets = 10 y = convert_to_one_hot(y, n_targets) # graph holds information necessary to build layers from parents graph = OrderedDict() X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph) # random state so script is deterministic random_state = np.random.RandomState(1999) minibatch_size = 20 n_hid = 1000
from collections import OrderedDict import numpy as np import theano from dagbldr.datasets import fetch_mnist, minibatch_iterator from dagbldr.optimizers import rmsprop from dagbldr.utils import add_datasets_to_graph, get_params_and_grads from dagbldr.utils import get_weights_from_graph from dagbldr.utils import convert_to_one_hot from dagbldr.utils import create_checkpoint_dict from dagbldr.utils import TrainingLoop from dagbldr.nodes import tanh_layer, softmax_zeros_layer from dagbldr.nodes import categorical_crossentropy mnist = fetch_mnist() train_indices = mnist["train_indices"] valid_indices = mnist["valid_indices"] X = mnist["data"] y = mnist["target"] n_targets = 10 y = convert_to_one_hot(y, n_targets) # graph holds information necessary to build layers from parents graph = OrderedDict() X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph) # random state so script is deterministic random_state = np.random.RandomState(1999) minibatch_size = 20 n_hid = 1000