np_l = LogisticRegression(W, b)
#########################
# THEANO PART
#########################
x_symbol = theano.tensor.dmatrix('x')
y_symbol = theano.tensor.ivector('y')
th_l = TheanoLogisticRegression(rng=np.random.RandomState(1234),
input=x_symbol,
n_in=10,
n_out=5,
W=theano.shared(value=W, name="W"),
b=theano.shared(value=b, name="b"))
f1 = theano.function(inputs=[x_symbol, y_symbol], outputs=th_l.nnl(y_symbol))
actual = np_l.nnl(x, y)
expected = f1(x, y)
assert_matrix_eq(actual, expected, "nnl")
f2 = theano.function(inputs=[x_symbol, y_symbol],
outputs=th_l.errors(y_symbol))
actual = np_l.errors(x, y)
expected = f2(x, y)
assert_matrix_eq(actual, expected, "errors")
th_l = TheanoLogisticRegression(rng = np.random.RandomState(1234),
input = x_symbol,
n_in = 10,
n_out = 5,
W = theano.shared(value = W,
name = "W"),
b = theano.shared(value = b,
name = "b")
)
f1 = theano.function(inputs = [x_symbol, y_symbol],
outputs = th_l.nnl(y_symbol)
)
actual = np_l.nnl(x, y)
expected = f1(x, y)
assert_matrix_eq(actual, expected, "nnl")
f2 = theano.function(inputs = [x_symbol, y_symbol],
outputs = th_l.errors(y_symbol)
)
actual = np_l.errors(x, y)
expected = f2(x, y)
assert_matrix_eq(actual, expected, "errors")
