feed_dict = {
softmax_model.weights_placeholder: Wout.T,
softmax_model.biases_placeholder: biases
}
session.run(init, feed_dict=feed_dict)
else:
RLS_delta = 0.000001
RLS_lambda = 0.9999995
SInverse = (1.0 / RLS_delta) * eye(total_res_size + input_size)
intermediate_data = []
for i, _ in enumerate(train_data):
print 'calculating reservoir states on text ' + str(i + 1) + '/' + str(
len(train_data)) + '...'
inputs, fw_states, bw_states = [], [], []
Xtr, Ytr, _, _, _, _ = format_data(train_data[i], embeddings,
embeddings_size, output_size,
window_size, synset2id, softmax)
trainLen = len(Xtr)
u_fw = zeros((input_size, 1))
if use_reservoirs == "True":
u_bw = zeros((input_size, 1))
x_fw = zeros((res_size, 1))
x_bw = zeros((res_size, 1))
state_fw = zeros((res_size + input_size, 1))
state_bw = zeros((res_size + input_size, 1))
for t in range(trainLen):
u_fw = reshape(asarray(Xtr[t]), (input_size, 1))
inputs.append(u_fw)
if use_reservoirs == "True":
u_bw = reshape(asarray(Xtr[trainLen - 1 - t]), (input_size, 1))
x_fw = (1 - a) * x_fw + a * tanh(
W_fw = cPickle.load(input_file)
W_bw = cPickle.load(input_file)
G_fw = cPickle.load(input_file)
G_bw = cPickle.load(input_file)
B_fw = cPickle.load(input_file)
B_bw = cPickle.load(input_file)
# test the trained ESN
print 'Testing...'
x = zeros((res_size, 1))
y = zeros((outSize, 1))
states = []
for i, text in enumerate(test_data):
inputs, fw_states, bw_states = [], [], []
Xts, Yts, lemmas, synsets, pos = format_data(test_data[i], embeddings,
embeddings_size,
window_size)
testLen = len(Xts)
u_fw = zeros((inSize, 1))
if use_reservoirs == "True":
u_bw = zeros((inSize, 1))
x_fw = zeros((res_size, 1))
x_bw = zeros((res_size, 1))
state_fw = zeros((res_size + inSize, 1))
state_bw = zeros((res_size + inSize, 1))
X = zeros((testLen, inSize + total_res_size, 1))
Y = zeros((testLen, outSize, 1))
inputs, fw_states, bw_states = [], [], []
for t in range(testLen):
u_fw = reshape(asarray(Xts[t]), (inSize, 1))
inputs.append(u_fw)
rhoW_bw = max(abs(linalg.eig(W_bw)[0]))
print 'done.'
# W *= 1.25 / rhoW
W_fw *= 1.25 / rhoW_fw
W_bw *= 1.25 / rhoW_bw
# RLS training
print 'Calculating reservoir states...'
RLS_delta = 0.000001
RLS_lambda = 0.9999995
SInverse = (1.0 / RLS_delta) * eye(total_res_size + inSize)
intermediate_data = []
for i,_ in enumerate(train_data):
print 'calculating reservoir states on text ' + str(i+1) + '/' + str(len(train_data)) + '...'
inputs, fw_states, bw_states = [], [], []
Xtr, Ytr,_, _, _, _ = format_data(train_data[i], embeddings, embeddings_size, window_size)
trainLen = len(Xtr)
u_fw = zeros((inSize, 1))
if use_reservoirs == "True":
u_bw = zeros((inSize, 1))
x_fw = zeros((res_size, 1))
x_bw = zeros((res_size, 1))
state_fw = zeros((res_size + inSize, 1))
state_bw = zeros((res_size + inSize, 1))
for t in range(trainLen):
u_fw = reshape(asarray(Xtr[t]), (inSize, 1))
inputs.append(u_fw)
if use_reservoirs == "True":
u_bw = reshape(asarray(Xtr[trainLen-1-t]), (inSize, 1))
x_fw = (1 - a) * x_fw + a * tanh(dot(Win_fw, u_fw) + dot(W_fw, x_fw))
x_bw = (1 - a) * x_bw + a * tanh(dot(Win_bw, u_bw) + dot(W_bw, x_bw))
Wout = cPickle.load(input_file)
if use_reservoirs == "True":
Win_fw = cPickle.load(input_file)
Win_bw = cPickle.load(input_file)
W_fw = cPickle.load(input_file)
W_bw = cPickle.load(input_file)
# test the trained ESN
print 'Testing...'
x = zeros((res_size, 1))
y=zeros((output_size ,1))
states = []
for i, text in enumerate(test_data):
inputs, fw_states, bw_states = [], [], []
Xts, Yts, lemmas, synsets, pos, term_id = format_data(test_data[i], embeddings, embeddings_size, output_size,
window_size, synset2id, softmax)
testLen = len(Xts)
u_fw = zeros((input_size, 1))
if use_reservoirs == "True":
u_bw = zeros((input_size, 1))
x_fw = zeros((res_size, 1))
x_bw = zeros((res_size, 1))
state_fw = zeros((res_size + input_size, 1))
state_bw = zeros((res_size + input_size, 1))
X = zeros((testLen, input_size + total_res_size, 1))
Y = zeros((testLen, output_size, 1))
inputs, fw_states, bw_states = [], [], []
for t in range(testLen):
u_fw = reshape(asarray(Xts[t]), (input_size, 1))
inputs.append(u_fw)
if use_reservoirs == "True":