dtype=np.int32
)
########### Embedding layer ##############
actual = f_el(x)
expected = dcnn.e_layer.output(x)
assert_matrix_eq(actual, expected, "Embedding")
########## Conv layer ###################
actual = dcnn._c_layer_output(x)
expected = f_cl(x)
assert_matrix_eq(actual, expected, "Conv")
########## Output layer ###################
actual = dcnn._p_y_given_x(x)
expected = f3(x)
assert_matrix_eq(actual, expected, "p_y_given_x")
########## errors ###########
actual = dcnn._errors(x, y)
expected = f2(x, y)
assert_about_eq(actual, expected, "errors")
########## nnl ###########
actual = dcnn._nnl(x, y)
expected = f1(x, y)
assert_about_eq(actual, expected, "nnl")
from dcnn import DCNN
from util import load_data
from param_util import load_dcnn_model_params
params = load_dcnn_model_params(
"models/filter_widths=8,6,,batch_size=10,,ks=20,8,,fold=1,1,,conv_layer_n=2,,ebd_dm=48,,l2_regs=1e-06,1e-06,1e-06,0.0001,,dr=0.5,0.5,,nkerns=7,12.pkl"
)
model = DCNN(params)
datasets = load_data("data/twitter.pkl")
dev_set_x, dev_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
dev_set_x, dev_set_y = dev_set_x.get_value(), dev_set_y.get_value()
test_set_x, test_set_y = test_set_x.get_value(), test_set_y.get_value()
print "dev error:", model._errors(dev_set_x, dev_set_y)
print "test error:", model._errors(test_set_x, test_set_y)
x = np.asarray(np.random.randint(vocab_size, size=(3, 6)), dtype=np.int32) y = np.asarray(np.random.randint(5, size=3), dtype=np.int32) ########### Embedding layer ############## actual = f_el(x) expected = dcnn.e_layer.output(x) assert_matrix_eq(actual, expected, "Embedding") ########## Conv layer ################### actual = dcnn._c_layer_output(x) expected = f_cl(x) assert_matrix_eq(actual, expected, "Conv") ########## Output layer ################### actual = dcnn._p_y_given_x(x) expected = f3(x) assert_matrix_eq(actual, expected, "p_y_given_x") ########## errors ########### actual = dcnn._errors(x, y) expected = f2(x, y) assert_about_eq(actual, expected, "errors") ########## nnl ########### actual = dcnn._nnl(x, y) expected = f1(x, y) assert_about_eq(actual, expected, "nnl")
from dcnn import DCNN
from util import load_data
from param_util import load_dcnn_model_params
params = load_dcnn_model_params("models/filter_widths=8,6,,batch_size=10,,ks=20,8,,fold=1,1,,conv_layer_n=2,,ebd_dm=48,,l2_regs=1e-06,1e-06,1e-06,0.0001,,dr=0.5,0.5,,nkerns=7,12.pkl")
model = DCNN(params)
datasets = load_data("data/twitter.pkl")
dev_set_x, dev_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
dev_set_x, dev_set_y = dev_set_x.get_value(), dev_set_y.get_value()
test_set_x, test_set_y = test_set_x.get_value(), test_set_y.get_value()
print "dev error:", model._errors(dev_set_x, dev_set_y)
print "test error:", model._errors(test_set_x, test_set_y)
