def lstm_temporal(x, h0, Wx, Wh, b):
"""
Forward pass for an LSTM over an entire sequence of data. We assume an input
sequence composed of T vectors, each of dimension D. The LSTM uses a hidden
size of H, and we work over a minibatch containing N sequences. After running
the LSTM forward, we return the hidden states for all timesteps.
Note that the initial cell state is passed as input, but the initial cell
state is set to zero. Also note that the cell state is not returned; it is
an internal variable to the LSTM and is not accessed from outside.
Inputs:
- x: Input data of shape (N, T, D)
- h0: Initial hidden state of shape (N, H)
- Wx: Weights for input-to-hidden connections, of shape (D, 4H)
- Wh: Weights for hidden-to-hidden connections, of shape (H, 4H)
- b: Biases of shape (4H,)
Returns a tuple of:
- h: Hidden states for all timesteps of all sequences, of shape (N, T, H)
"""
N, T, D = x.shape
_, H = h0.shape
c = np.zeros([N, 0, H])
h = np.zeros([N, 0, H])
for t in xrange(T):
h_step, c_step = lstm_step(
x[:, t, :], h[:, t - 1, :] if t > 0 else h0,
c[:, t - 1, :] if t > 0 else np.zeros((N, H)), Wx, Wh, b)
h_step = h_step.reshape(N, 1, H)
c_step = c_step.reshape(N, 1, H)
h = np.append(h, h_step, axis=1)
c = np.append(c, c_step, axis=1)
return h
def lstm_temporal(x, h0, Wx, Wh, b):
"""
Forward pass for an LSTM over an entire sequence of data. We assume an input
sequence composed of T vectors, each of dimension D. The LSTM uses a hidden
size of H, and we work over a minibatch containing N sequences. After running
the LSTM forward, we return the hidden states for all timesteps.
Note that the initial cell state is passed as input, but the initial cell
state is set to zero. Also note that the cell state is not returned; it is
an internal variable to the LSTM and is not accessed from outside.
Inputs:
- x: Input data of shape (N, T, D)
- h0: Initial hidden state of shape (N, H)
- Wx: Weights for input-to-hidden connections, of shape (D, 4H)
- Wh: Weights for hidden-to-hidden connections, of shape (H, 4H)
- b: Biases of shape (4H,)
Returns a tuple of:
- h: Hidden states for all timesteps of all sequences, of shape (N, T, H)
"""
N, T, D = x.shape
_, H = h0.shape
c = np.zeros([N, 0, H])
h = np.zeros([N, 0, H])
for t in xrange(T):
h_step, c_step = lstm_step(
x[:, t, :], h[:, t-1, :] if t > 0 else h0, c[:, t-1, :] if t > 0 else np.zeros((N, H)), Wx, Wh, b)
h_step = h_step.reshape(N, 1, H)
c_step = c_step.reshape(N, 1, H)
h = np.append(h, h_step, axis=1)
c = np.append(c, c_step, axis=1)
return h
def rnn_temporal(x, h0, Wx, Wh, b):
"""
Run a vanilla RNN forward on an entire sequence of data. We assume an input
sequence composed of T vectors, each of dimension D. The RNN uses a hidden
size of H, and we work over a minibatch containing N sequences. After running
the RNN forward, we return the hidden states for all timesteps.
Inputs:
- x: Input data for the entire timeseries, of shape (N, T, D).
- h0: Initial hidden state, of shape (N, H)
- Wx: Weight matrix for input-to-hidden connections, of shape (D, H)
- Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H)
- b: Biases of shape (H,)
Returns a tuple of:
- h: Hidden states for the entire timeseries, of shape (N, T, H).
"""
N, T, D = x.shape
H = h0.shape[1]
h = np.zeros([N, 0, H])
for t in xrange(T):
h_step = rnn_step(x[:, t, :], h0 if t == 0 else h[:, t - 1, :], Wx, Wh,
b).reshape(N, 1, H)
h = np.append(h, h_step, axis=1)
return h
def rnn_temporal(x, h0, Wx, Wh, b):
"""
Run a vanilla RNN forward on an entire sequence of data. We assume an input
sequence composed of T vectors, each of dimension D. The RNN uses a hidden
size of H, and we work over a minibatch containing N sequences. After running
the RNN forward, we return the hidden states for all timesteps.
Inputs:
- x: Input data for the entire timeseries, of shape (N, T, D).
- h0: Initial hidden state, of shape (N, H)
- Wx: Weight matrix for input-to-hidden connections, of shape (D, H)
- Wh: Weight matrix for hidden-to-hidden connections, of shape (H, H)
- b: Biases of shape (H,)
Returns a tuple of:
- h: Hidden states for the entire timeseries, of shape (N, T, H).
"""
N, T, _ = x.shape
H = h0.shape[1]
h = np.zeros([N, 0, H])
for t in range(T):
h_step = rnn_step(x[:, t, :], h0 if t == 0 else h[:, t - 1, :], Wx, Wh,
b).reshape(N, 1, H)
h = np.append(h, h_step, axis=1)
return h
