'incoming_layer_list': [0,],
'incoming_weight_list': [],
'bias': None,
'loss': 'cross_entropy',
'act_func_name': 'softmax',
'value': None,
'layer_type': 'output',
'back_error': 0,
'link2input': None,
'link2target': y_train } }
network = NeuralNetwork(n_layers=2, layer_dict = Networklayer_dict)
network.fit(batch_size = 1000, learning_rate = step_iterator(0.1,0.01,-0.02),
weight_decay = step_iterator(0,0,0), momentum = step_iterator(0.1,0.9,0.1), n_iter = 100, switch_point = 10)
y_pred = network.transform(rbm2.transform(rbm1.transform(rbm0.transform(X_test))))[0]
correct = np.sum(y_pred.argmax(axis=1) == y_test.argmax(axis=1))
print('correct = %d in %d'%(correct,X_test.shape[0]))
network.transform(rbm2.transform(rbm1.transform(rbm0.transform(X_train_copy))))[0]
error = network.empirical_error(target = y_train)
print('initial error: %f'%error)
with open(r"C:\Users\daredavil\Documents\Python Scripts\RBMver2\rbms.pkl",'wb') as file_:
pickle.dump((rbm0.hidden_layer.dimension, rbm0.weight_list[0], rbm0.hidden_layer.bias,
rbm1.hidden_layer.dimension, rbm1.weight_list[0], rbm1.hidden_layer.bias,
rbm2.hidden_layer.dimension, rbm2.weight_list[0], rbm2.hidden_layer.bias,
network.output_layer_list[0].incoming_weight_list[0], network.output_layer_list[0].bias), file_)
Networklayer_dict = { 0: {
'n_neuron': X.shape[1],
"bias": rbm_list[0].input_layer_list[0].bias,
"loss": "mse",
"act_func_name": "linear",
"value": None,
"layer_type": "output",
"random_state": random_state,
"back_error": 0,
"link2target": X_train,
},
}
network = NeuralNetwork(n_layers=11, layer_dict=Networklayer_dict)
network.fit(
batch_size=1000,
learning_rate=step_iterator(0.1, 0.01, -0.02),
weight_decay=step_iterator(0, 0, 0),
momentum=step_iterator(0, 0, 0),
n_iter=5,
switch_point=None,
)
network.transform([X_train])[0]
hidd_rep_train = network.layer_list[5].value
network.transform([X_test])[0]
hidd_rep_test = network.layer_list[5].value
save_dict["hrtrain"] = hidd_rep_train
save_dict["hrtest"] = hidd_rep_test
with open("data.pkl", "wb") as f_:
cPickle.dump(save_dict, f_)