X, Z, hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared', optimizer=optimizer, batch_size=batch_size, max_iter=max_iter) elif typ == 'fd': m = FastDropoutNetwork(2099, [800, 800], 14, X, Z, TX, TZ, hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared', p_dropout_inpt=.1, p_dropout_hiddens=.2, optimizer=optimizer, batch_size=batch_size, max_iter=max_iter) #climin.initialize.randomize_normal(m.parameters.data, 0, 1 / np.sqrt(m.n_inpt)) m.init_weights() #Transform the test data #TX = m.transformedData(TX) TX = np.array([TX for _ in range(10)]).mean(axis=0) print TX.shape
1, X, Z, hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared', optimizer=optimizer, batch_size=batch_size, max_iter=max_iter) elif typ == 'fd': m = FastDropoutNetwork(X.shape[1], [100, 100], 1, X, Z, hidden_transfers=['tanh', 'tanh'], out_transfer='identity', loss='squared', p_dropout_inpt=.1, p_dropout_hiddens=.2, optimizer=optimizer, batch_size=batch_size, max_iter=max_iter) #climin.initialize.randomize_normal(m.parameters.data, 0, 1 / np.sqrt(m.n_inpt)) m.init_weights() #Transform the test data #TX = m.transformedData(TX) TX = np.array([TX for _ in range(10)]).mean(axis=0) print TX.shape losses = []