def train(x_train, y_train, alpha=0.001, weight_decay=0.0, epochs=40):
for i in range(epochs):
train_loss = 0
count = 0
for j in range(img_num):
input = x_train[j].copy().reshape(1, 784) / 255.0
target = y_train[j].copy()
#Extracting hidden features using RBM
input, _ = model_test.sample_h_given_v(input)
#Forward
linear = Linear.forward(input)
loss = cross_entropy(linear, target)
train_loss += loss
#Backward
prob = np.exp(linear) / np.sum(np.exp(linear), axis=1)
prob[0, target] -= 1
Linear.backward(input, prob, alpha, weight_decay)
#Training Accuracy
train_prob = np.exp(linear)
if np.argmax(train_prob) == target:
count += 1
#Testing Accuracy
num_test = 10000
test_count = 0
for k in range(num_test):
test_input = x_test[k].copy().reshape(1, 784) / 255.0
test_input, _ = model_test.sample_h_given_v(test_input)
test_target = y_test[k].copy()
test_prob = Linear.forward(test_input)
test_prob = np.exp(test_prob)
if np.argmax(test_prob) == test_target:
test_count += 1
print("Epoch", i, " ", "Loss", " ", train_loss / img_num, "train_Acc",
" ", count / img_num, "test_Acc", " ", test_count / num_test)
classifier_weight_rbm = open(b"model/classifier_weight_rbm.npy", "wb")
pickle.dump(Linear.weight, classifier_weight_rbm)
classifier_bias_rbm = open(b"model/classifier_bias_rbm.npy", "wb")
pickle.dump(Linear.bias, classifier_bias_rbm)
class RNNAutoencoder:
def __init__(self, vac_size: int, hidden_sizes: Tuple[int, int],
seq_size: int):
"""
Class implements RNNAutoencoder.
Architecture of RNNAutoencoder have 2 lstm layers in encoder and
2 lstm layers with linear layer in decoder
:param vac_size: int
:param hidden_sizes: Tuple[int, int]
:param seq_size: int
"""
self.vac_size = vac_size
self.hidden_size_1 = hidden_sizes[0]
self.hidden_size_2 = hidden_sizes[1]
self.seq_size = seq_size
#Encode
self.lstm1 = LSTMCell(vac_size=self.vac_size,
hidden_size=self.hidden_size_1,
return_seq=True)
self.lstm2 = LSTMCell(vac_size=self.hidden_size_1,
hidden_size=self.hidden_size_2,
return_seq=False)
self.repeat = RepeatVector(self.seq_size)
#Decode
self.lstm3 = LSTMCell(self.hidden_size_2,
self.hidden_size_1,
return_seq=True)
self.lstm4 = LSTMCell(self.hidden_size_1,
self.vac_size,
return_seq=True)
self.linear = Linear(self.vac_size, self.vac_size)
def params(self):
"""
returns parameters of all model
:return: dict
"""
return {
'lstm1': self.lstm1.params(),
'lstm2': self.lstm2.params(),
'lstm3': self.lstm3.params(),
'lstm4': self.lstm4.params(),
'linear': self.linear.params()
}
def clear_gradients(self):
"""
function which clears gradients
:return:
"""
self.lstm1.clear_gradients()
self.lstm2.clear_gradients()
self.lstm3.clear_gradients()
self.lstm4.clear_gradients()
self.linear.clear_gradients()
def forward(self, X: np.ndarray):
"""
forward pass through the model
:param X: np.ndarray
:return: predictions of model
"""
self.clear_gradients()
encode = self.lstm2.forward(self.lstm1.forward(X))
bridge = self.repeat.forward(encode)
decode = self.lstm4.forward(self.lstm3.forward(bridge))
decode = decode.reshape(decode.shape[0], decode.shape[1])
pred = self.linear.forward(decode)
return pred
def compute_loss_and_gradient(self, X: np.ndarray, y: np.ndarray):
"""
function which implement forward pass and calculation of loss and its derivative
:param X: not-sorted one-hot array (seq_size, vac_size, 1)
:param y: sorted sequence (seq_size, )
:return: loss and its derivative
"""
pred = self.forward(X)
loss, dpredication = softmax_cross_entropy(pred, y)
return loss, dpredication
def repeat_backward(self, x: np.ndarray):
"""
function which repeat vector for backward pass
:param x: np.ndarray size (vac_size, 1)
:return: d_out :np.ndarray size(seq_size, vac_size, 1)
"""
d_out = np.zeros((self.seq_size, *x.shape))
d_out[-1] = x
return d_out
def backward(self, d_out: np.ndarray):
"""
backward pass through model
:param d_out: derivative of loss
:return:
"""
d_l = self.linear.backward(d_out)
d_l = d_l.reshape(*d_l.shape, 1)
d_l = self.lstm3.backward(self.lstm4.backward(d_l))
bridge = self.repeat_backward(self.repeat.backward(d_l))
d_x = self.lstm1.backward(self.lstm2.backward(bridge))
return d_x
def predict(self, X: np.ndarray):
"""
predict answer of the model
:param X:
:return:
"""
pred = self.forward(X)
probs = softmax(pred)
return np.argmax(probs, axis=1)
