def create_model(self):
self.X = [T.imatrix(('X_%d' %i)) for i in range(self.num_of_corpus)]
self.X_input = T.concatenate([T.tanh(T.dot(basic.row_normalize(self.X[i]*self.idf), self.W[i])) for i in range(self.num_of_corpus)], axis=0)
self.X_c_input = T.concatenate([T.tanh(T.dot(basic.row_normalize(self.X[i]*self.idf), self.W_c)) for i in range(self.num_of_corpus)], axis=0)
self.X_all_input = T.concatenate([self.X_input, self.X_c_input], axis=0)
self.y_input = T.concatenate([i*T.ones_like(self.X[i][:,0], dtype='int32') for i in range(self.num_of_corpus)])
self.z_input = T.concatenate([T.ones_like(self.X_input[:,0], dtype='int32'), T.zeros_like(self.X_c_input[:,0], dtype='int32')])
self.classifier = basic.MLP(self.rng, self.X_c_input, self.y_input, n_out=self.num_of_corpus)
self.discriminator = basic.MLP(self.rng, self.X_all_input, self.z_input, n_hidden=10, n_out=2, L2_reg=0.01)
self.generator = [self.W_c]
def create_model(self):
self.X = [T.imatrix(('X_%d' % i)) for i in range(self.num_of_corpus)]
self.X_input = T.concatenate([
(T.dot(basic.row_normalize(self.X[i] * self.idf), self.W) + self.b)
for i in range(self.num_of_corpus)
],
axis=0)
self.generator = Generator(self.rng, self.theano_rng, self.batch_size,
self.num_of_corpus)
self.X_all_input = T.concatenate(
[self.X_input, (T.dot(self.generator.output, self.W) + self.b)],
axis=0)
self.y_input = T.concatenate([
i * T.ones_like(self.X[i][:, 0], dtype='int32')
for i in range(self.num_of_corpus)
])
self.g_label_input = T.concatenate([
(i + self.num_of_corpus) * T.ones_like(
self.generator.output[:self.generator.batch_size_var, 0],
dtype='int32') for i in range(self.num_of_corpus)
])
self.y_all_input = T.concatenate([self.y_input, self.g_label_input])
self.discriminator = basic.MLP(self.rng,
self.X_all_input,
self.y_all_input,
n_out=2 * self.num_of_corpus)
self.classifier = basic.MLP(self.rng,
self.X_input,
self.y_input,
n_out=self.num_of_corpus)
def create_model(self):
self.X = [T.imatrix(('X_%d' %i)) for i in range(self.num_of_corpus)]
self.X_c_input = T.concatenate([T.tanh(T.dot(basic.row_normalize(self.X[i]*self.idf), self.W_c) + self.b_c) for i in range(self.num_of_corpus)], axis=0)
self.y_input = T.concatenate([i*T.ones_like(self.X[i][:,0], dtype='int32') for i in range(self.num_of_corpus)])
self.classifier = basic.MLP(self.rng, self.X_c_input, self.y_input, n_out=self.num_of_corpus)
self.classifier_params = self.classifier.params + [self.W_c, self.b_c]