from collections import OrderedDict
import numpy as np
import theano
from dagbldr.utils import add_datasets_to_graph, convert_to_one_hot
from dagbldr.utils import get_params_and_grads
from dagbldr.utils import early_stopping_trainer
from dagbldr.nodes import linear_layer, softmax_layer
from dagbldr.nodes import categorical_crossentropy
from dagbldr.optimizers import sgd
from dagbldr.datasets import load_digits
# Common between tests
digits = load_digits()
X = digits["data"]
y = digits["target"]
n_classes = len(set(y))
y = convert_to_one_hot(y, n_classes)
def test_feedforward_classifier():
minibatch_size = 100
random_state = np.random.RandomState(1999)
graph = OrderedDict()
X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph)
l1_o = linear_layer([X_sym], graph, 'l1', proj_dim=20,
random_state=random_state)
y_pred = softmax_layer([l1_o], graph, 'pred', n_classes,
random_state=random_state)
from dagbldr.datasets import load_digits
from dagbldr.optimizers import sgd
from dagbldr.utils import add_embedding_datasets_to_graph, convert_to_one_hot
from dagbldr.utils import add_datasets_to_graph
from dagbldr.utils import get_params_and_grads, make_embedding_minibatch
from dagbldr.nodes import fixed_projection_layer, embedding_layer
from dagbldr.nodes import projection_layer, linear_layer, softmax_layer
from dagbldr.nodes import softmax_zeros_layer, maxout_layer
from dagbldr.nodes import sigmoid_layer, tanh_layer, softplus_layer
from dagbldr.nodes import exp_layer, relu_layer, dropout_layer
from dagbldr.nodes import softmax_sample_layer, gaussian_sample_layer
from dagbldr.nodes import gaussian_log_sample_layer, conv2d_layer
from dagbldr.nodes import pool2d_layer
# Common between tests
digits = load_digits()
X = digits["data"]
y = digits["target"]
n_classes = len(set(y))
y = convert_to_one_hot(y, n_classes)
def run_common_layer(layer):
random_state = np.random.RandomState(42)
graph = OrderedDict()
X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph)
single_o = layer([X_sym], graph, 'single', proj_dim=5,
random_state=random_state)
concat_o = layer([X_sym, y_sym], graph, 'concat', proj_dim=5,
random_state=random_state)
# Check that things can be reused
def test_digits():
digits = load_digits()
assert_equal(len(digits["data"]), len(digits["target"]))
