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")