n_steps = 45
seq_width = 50
initializer = tf.random_uniform_initializer(-1,1)
seq_input = tf.placeholder(tf.float32, [n_steps, batch_size, seq_width])
#sequence we will provide at runtime
early_stop = tf.placeholder(tf.int32)
#what timestep we want to stop at
inputs = [tf.reshape(i, (batch_size, seq_width)) for i in tf.split(0, n_steps, seq_input)]
#inputs for rnn needs to be a list, each item being a timestep.
#we need to split our input into each timestep, and reshape it because split keeps dims by default
cell = LSTMCell(size, seq_width, initializer=initializer)
initial_state = cell.zero_state(batch_size, tf.float32)
outputs, states = rnn.rnn(cell, inputs, initial_state=initial_state, sequence_length=early_stop)
#set up lstm
iop = tf.initialize_all_variables()
#create initialize op, this needs to be run by the session!
session = tf.Session()
session.run(iop)
#actually initialize, if you don't do this you get errors about uninitialized stuff
feed = {early_stop:100, seq_input:np.random.rand(n_steps, batch_size, seq_width).astype('float32')}
#define our feeds.
#early_stop can be varied, but seq_input needs to match the shape that was defined earlier
outs = session.run(outputs, feed_dict=feed)
#run once
#sequence we will provide at runtime
early_stop = tf.placeholder(tf.int32)
#what timestep we want to stop at
embedding_matrix = tf.Variable(tf.zeros([n_vocab, seq_width]))
W_class = tf.Variable(tf.zeros([seq_width, n_class]))
B_class = tf.Variable(tf.zeros([n_class, ]))
embed_input = tf.nn.embedding_lookup(embedding_matrix, seq_input)
inputs = [tf.reshape(i, (batch_size, seq_width)) for i in tf.split(0, n_steps, embed_input)]
#inputs for rnn needs to be a list, each item being a timestep.
#we need to split our input into each timestep, and reshape it because split keeps dims by default
#cell = LSTMCell(seq_width, seq_width, initializer=initializer)
cell = LSTMCell(seq_width, seq_width, initializer=initializer)
initial_state = cell.zero_state(batch_size, tf.float32)
outputs, states = rnn.rnn(cell, inputs, initial_state=initial_state, sequence_length=early_stop)
last = outputs[-1]
logit = tf.matmul(last, W_class) + B_class
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logit, y))
iop = tf.initialize_all_variables()
#create initialize op, this needs to be run by the session!
#session = tf.Session()
session = tf.Session(config=tf.ConfigProto(log_device_placement=True))
session.run(iop)
#actually initialize, if you don't do this you get errors about uninitialized stuff
feed = {early_stop:n_steps,
# What timestep we want to stop at
early_stop = tf.placeholder(tf.int32)
initializer = tf.random_uniform_initializer(-1, 1)
# Inputs for rnn needs to be a list, each item being a timestep.
# we need to split our input into each timestep, and reshape it because
# split keeps dims by default
inputs = [
tf.reshape(i, (batch_size, input_dim))
for i in tf.split(0, n_steps, seq_input)
]
with tf.device("/cpu:0"):
cell1 = LSTMCell(hidden_dim, input_dim, initializer=initializer)
initial_state1 = cell1.zero_state(batch_size, tf.float32)
outputs1, states1 = rnn.rnn(cell1,
inputs,
initial_state=initial_state1,
sequence_length=early_stop,
scope="RNN1")
with tf.device("/cpu:0"):
cell2 = LSTMCell(output_dim, hidden_dim, initializer=initializer)
initial_state2 = cell2.zero_state(batch_size, tf.float32)
outputs2, states2 = rnn.rnn(cell2,
outputs1,
initial_state=initial_state2,
sequence_length=early_stop,
scope="RNN2")
Fx,Fy,gamma=attn_window("write",h_dec,write_n)
Fyt=tf.transpose(Fy,perm=[0,2,1])
wr=tf.batch_matmul(Fyt,tf.batch_matmul(w,Fx))
wr=tf.reshape(wr,[batch_size,B*A])
#gamma=tf.tile(gamma,[1,B*A])
return wr*tf.reshape(1.0/gamma,[-1,1])
write=write_attn if FLAGS.write_attn else write_no_attn
## STATE VARIABLES ##
cs=[0]*T # sequence of canvases
mus,logsigmas,sigmas=[0]*T,[0]*T,[0]*T # gaussian params generated by SampleQ. We will need these for computing loss.
# initial states
h_dec_prev=tf.zeros((batch_size,dec_size))
enc_state=lstm_enc.zero_state(batch_size, tf.float32)
dec_state=lstm_dec.zero_state(batch_size, tf.float32)
## DRAW MODEL ##
# construct the unrolled computational graph
for t in range(T):
c_prev = tf.zeros((batch_size,img_size)) if t==0 else cs[t-1]
x_hat=x-tf.sigmoid(c_prev) # error image
r=read(x,x_hat,h_dec_prev)
h_enc,enc_state=encode(enc_state,tf.concat(1,[r,h_dec_prev]))
z,mus[t],logsigmas[t],sigmas[t]=sampleQ(h_enc)
h_dec,dec_state=decode(dec_state,z)
cs[t]=c_prev+write(h_dec) # store results
h_dec_prev=h_dec
DO_SHARE=True # from now on, share variables
wr = tf.reshape(wr, [batch_size, B * A])
#gamma=tf.tile(gamma,[1,B*A])
return wr * tf.reshape(1.0 / gamma, [-1, 1])
write = write_attn if FLAGS.write_attn else write_no_attn
## STATE VARIABLES ##
cs = [0] * T # sequence of canvases
mus, logsigmas, sigmas = [0] * T, [0] * T, [
0
] * T # gaussian params generated by SampleQ. We will need these for computing loss.
# initial states
h_dec_prev = tf.zeros((batch_size, dec_size))
enc_state = lstm_enc.zero_state(batch_size, tf.float32)
dec_state = lstm_dec.zero_state(batch_size, tf.float32)
## DRAW MODEL ##
# construct the unrolled computational graph
for t in range(T):
c_prev = tf.zeros((batch_size, img_size)) if t == 0 else cs[t - 1]
x_hat = x - tf.sigmoid(c_prev) # error image
r = read(x, x_hat, h_dec_prev)
h_enc, enc_state = encode(enc_state, tf.concat(1, [r, h_dec_prev]))
z, mus[t], logsigmas[t], sigmas[t] = sampleQ(h_enc)
h_dec, dec_state = decode(dec_state, z)
cs[t] = c_prev + write(h_dec) # store results
h_dec_prev = h_dec
DO_SHARE = True # from now on, share variables