def read(folder):
log.info('Reading pretrained network from {}'.format(folder))
saver = Saver(folder)
ckpt_info = saver.get_ckpt_info()
model_opt = ckpt_info['model_opt']
ckpt_fname = ckpt_info['ckpt_fname']
model_id = ckpt_info['model_id']
m = model.get_model(model_opt)
cnn_nlayers = len(model_opt['cnn_filter_size'])
mlp_nlayers = 1
timespan = 1
weights = {}
sess = tf.Session()
saver.restore(sess, ckpt_fname)
output_list = []
for net, nlayers in zip(['cnn', 'mlp'], [cnn_nlayers, mlp_nlayers]):
for ii in xrange(nlayers):
for w in ['w', 'b']:
key = '{}_{}_{}'.format(net, w, ii)
log.info(key)
output_list.append(key)
if net == 'cnn':
for tt in xrange(timespan):
for w in ['beta', 'gamma']:
key = '{}_{}_{}_{}'.format(net, ii, tt, w)
log.info(key)
output_list.append(key)
output_var = []
for key in output_list:
output_var.append(m[key])
output_var_value = sess.run(output_var)
for key, value in zip(output_list, output_var_value):
weights[key] = value
log.info(key)
log.info(value.shape)
return weights
def read(folder):
log.info('Reading pretrained network from {}'.format(folder))
saver = Saver(folder)
ckpt_info = saver.get_ckpt_info()
model_opt = ckpt_info['model_opt']
ckpt_fname = ckpt_info['ckpt_fname']
model_id = ckpt_info['model_id']
m = model.get_model(model_opt)
cnn_nlayers = len(model_opt['cnn_filter_size'])
mlp_nlayers = 1
timespan = 1
weights = {}
sess = tf.Session()
saver.restore(sess, ckpt_fname)
output_list = []
for net, nlayers in zip(['cnn', 'mlp'], [cnn_nlayers, mlp_nlayers]):
for ii in xrange(nlayers):
for w in ['w', 'b']:
key = '{}_{}_{}'.format(net, w, ii)
log.info(key)
output_list.append(key)
if net == 'cnn':
for tt in xrange(timespan):
for w in ['beta', 'gamma']:
key = '{}_{}_{}_{}'.format(net, ii, tt, w)
log.info(key)
output_list.append(key)
output_var = []
for key in output_list:
output_var.append(m[key])
output_var_value = sess.run(output_var)
for key, value in zip(output_list, output_var_value):
weights[key] = value
log.info(key)
log.info(value.shape)
return weights
plt.tight_layout(pad=2.0, w_pad=0.0, h_pad=0.0)
plt.savefig(fname, dpi=150)
plt.close('all')
def preprocess(x1, x2, y):
"""Preprocess training data."""
return (x1.astype('float32') / 255,
x2.astype('float32') / 255,
y.astype('float32'))
if __name__ == '__main__':
restore_folder = sys.argv[1]
saver = Saver(restore_folder)
ckpt_info = saver.get_ckpt_info()
model_opt = ckpt_info['model_opt']
data_opt = ckpt_info['data_opt']
ckpt_fname = ckpt_info['ckpt_fname']
step = ckpt_info['step']
model_id = ckpt_info['model_id']
log.info('Building model')
m = get_model(model_opt)
log.info('Loading dataset')
dataset = get_dataset(data_opt)
sess = tf.Session()
saver.restore(sess, ckpt_fname)
