nprnn.py
def forward(input, state):
	'''
	Computes only the forward pass through one step of the time, note that the input to the softmax is divided by a hyperparameter TEMPERATURE
	-input is an index of the char in vocabulary
	-state, the same as for forward_backward, but the BATCH_SIZE is 1, so the final shape is [DEPTH, 1, HIDDEN_LAYER_SIZE] 
	
	Returns probabilities and the updated state of the hidden units
	'''
	ox = np.zeros((1, VOCABULARY_SIZE))
	ox[0, input] = 1

	for d in xrange(DEPTH):
		state[d] = relu(np.dot(ox, Wxh[d]) + np.dot(state[d], Whh[d]) + bh[d])
		ox = state[d]
		
	y = np.dot(ox, Why) + by
	y = np.clip(y, -100, 100)
	oz = softmax(y / TEMPERATURE)
	return np.reshape(oz, (VOCABULARY_SIZE)), state

	Computes only the forward pass through one step of the time, note that the input to the softmax is divided by a hyperparameter TEMPERATURE
	-input is an index of the char in vocabulary
	-state, the same as for forward_backward, but the BATCH_SIZE is 1, so the final shape is [DEPTH, 1, HIDDEN_LAYER_SIZE] 
	
	Returns probabilities and the updated state of the hidden units
	
Computes only the forward pass through one step of the time, note that the input to the softmax is divided by a hyperparameter TEMPERATURE
input is an index of the char in vocabulary
state, the same as for forward_backward, but the BATCH_SIZE is 1, so the final shape is [DEPTH, 1, HIDDEN_LAYER_SIZE]

Returns probabilities and the updated state of the hidden units
Computes only the forward pass through one step of the time note that the input to the softmax is divided by a hyperparameter TEMPERATURE input is an index of the char in vocabulary state the same as for forward_backward but the BATCH_SIZE is 1 so the final shape is [ DEPTH 1 HIDDEN_LAYER_SIZE ] Returns probabilities and the updated state of the hidden units
def forward(input, state):
	ox = np.zeros((1, VOCABULARY_SIZE))
	ox[0, input] = 1
	for d in xrange(DEPTH):
		state[d] = relu(np.dot(ox, Wxh[d]) + np.dot(state[d], Whh[d]) + bh[d])
		ox = state[d]
	y = np.dot(ox, Why) + by
	y = np.clip(y, -100, 100)
	oz = softmax(y / TEMPERATURE)
	return np.reshape(oz, (VOCABULARY_SIZE)), state
def forward ( input , state ) : ox = np . zeros ( ( 1 , VOCABULARY_SIZE ) ) ox [ 0 , input ] = 1 for d in xrange ( DEPTH ) : state [ d ] = relu ( np . dot ( ox , Wxh [ d ] ) + np . dot ( state [ d ] , Whh [ d ] ) + bh [ d ] ) ox = state [ d ] y = np . dot ( ox , Why ) + by y = np . clip ( y , - 100 , 100 ) oz = softmax ( y / TEMPERATURE ) return np . reshape ( oz , ( VOCABULARY_SIZE ) ) , state
Computes only the forward pass through one step of the time, note that the input to the softmax is divided by a hyperparameter TEMPERATURE
input is an index of the char in vocabulary
state, the same as for forward_backward, but the BATCH_SIZE is 1, so the final shape is [DEPTH, 1, HIDDEN_LAYER_SIZE]
Computes only the forward pass through one step of the time note that the input to the softmax is divided by a hyperparameter TEMPERATURE input is an index of the char in vocabulary state the same as for forward_backward but the BATCH_SIZE is 1 so the final shape is [ DEPTH 1 HIDDEN_LAYER_SIZE ]
'''
	Computes only the forward pass through one step of the time, note that the input to the softmax is divided by a hyperparameter TEMPERATURE
	-input is an index of the char in vocabulary
	-state, the same as for forward_backward, but the BATCH_SIZE is 1, so the final shape is [DEPTH, 1, HIDDEN_LAYER_SIZE] 
	
	Returns probabilities and the updated state of the hidden units
	'''
input
state