def initParams(self):
np.random.seed(12341)
# Word vectors
#self.L = 0.01*np.random.randn(self.wvecDim,self.numWords)
self.L = load_word_vectors().T # shape is (num_words, word_dim)
# Hidden activation weights for layer 1
self.W1 = 0.01*np.random.randn(self.wvecDim,2*self.wvecDim)
self.b1 = np.zeros((self.wvecDim))
# Hidden activation weights for layer 2
self.W2 = 0.01*np.random.randn(self.middleDim,self.wvecDim)
self.b2 = np.zeros((self.middleDim))
# Softmax weights
self.Ws = 0.01*np.random.randn(self.outputDim,self.middleDim) # note this is " U " in the notes and the handout.. there is a reason for the change in notation
self.bs = np.zeros((self.outputDim))
self.stack = [self.L, self.W1, self.b1, self.W2, self.b2, self.Ws, self.bs]
# Gradients
self.dW1 = np.empty(self.W1.shape)
self.db1 = np.empty((self.wvecDim))
self.dW2 = np.empty(self.W2.shape)
self.db2 = np.empty((self.middleDim))
self.dWs = np.empty(self.Ws.shape)
self.dbs = np.empty((self.outputDim))
def initParams(self):
np.random.seed(12341)
# Word vectors
#self.L = 0.01*np.random.randn(self.wvecDim,self.numW)
self.L = load_word_vectors().T # shape is (num_words, word_dim)
# Hidden layer parameters
MULT = 0.01
self.W = MULT * np.random.randn(self.wvecDim, 2 * self.wvecDim)
self.b = np.zeros((self.wvecDim))
# Softmax weights
self.Ws = MULT * np.random.randn(self.outputDim, self.wvecDim) # U
self.bs = np.zeros((self.outputDim))
self.stack = [self.L, self.W, self.b, self.Ws, self.bs]
# Gradients
self.dW = np.empty(self.W.shape)
self.db = np.empty((self.wvecDim))
self.dWs = np.empty(self.Ws.shape)
self.dbs = np.empty((self.outputDim))
def initParams(self):
np.random.seed(12341)
# Word vectors
#self.L = 0.01*np.random.randn(self.wvecDim,self.numW)
self.L = load_word_vectors().T # shape is (num_words, word_dim)
# Hidden layer parameters
MULT = 0.01
self.W = MULT * np.random.randn(self.wvecDim, 2 * self.wvecDim)
self.b = np.zeros((self.wvecDim))
# Softmax weights
self.Ws = MULT * np.random.randn(self.outputDim, self.wvecDim) # U
self.bs = np.zeros((self.outputDim))
self.stack = [self.L, self.W, self.b, self.Ws, self.bs]
# Gradients
self.dW = np.empty(self.W.shape)
self.db = np.empty((self.wvecDim))
self.dWs = np.empty(self.Ws.shape)
self.dbs = np.empty((self.outputDim))
