Created
March 8, 2019 17:51
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def get_model_params(self): | |
# get trainable params. | |
model_names = [] | |
model_params = [] | |
model_shapes = [] | |
with self.g.as_default(): | |
t_vars = tf.trainable_variables() | |
for var in t_vars: | |
param_name = var.name | |
p = self.sess.run(var) | |
model_names.append(param_name) | |
params = np.round(p*10000).astype(np.int).tolist() # ..?! | |
model_params.append(params) | |
model_shapes.append(p.shape) | |
return model_params, model_shapes, model_names | |
def set_model_params(self, params): | |
with self.g.as_default(): | |
trainable_vars = tf.trainable_variables() | |
idx = 0 | |
for var in trainable_vars: | |
t_shape = self.sess.run(var).shape | |
p_ = np.array(params[idx]) | |
assert t_shape == p_.shape, "inconsistent shape" | |
assign_op = var.assign(p_.astype(np.float)/10000.) | |
self.sess.run(assign_op) | |
idx += 1 | |
def save_json(self, jsonfile='rnn.json'): | |
model_params, model_shapes, model_names = self.get_model_params() | |
qparams = [] | |
for p in model_params: | |
qparams.append(p) | |
with open(jsonfile, 'wt') as outfile: | |
json.dump(qparams, outfile, sort_keys=True, indent=0, separators=(',', ': ')) | |
def load_json(self, jsonfile='rnn.json'): | |
with open(jsonfile, 'r') as f: | |
params = json.load(f) | |
self.set_model_params(params) |
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