def set_param_values(self, flattened_params, **tags): #import ipdb #ipdb.set_trace() debug = tags.pop("debug", False) # import ipdb # ipdb.set_trace() param_values = unflatten_tensors(flattened_params, self.get_param_shapes(**tags)) ops = [] feed_dict = dict() for param, dtype, value in zip(self.get_params(**tags), self.get_param_dtypes(**tags), param_values): if param not in self._cached_assign_ops: assign_placeholder = tf.placeholder( dtype=param.dtype.base_dtype) assign_op = tf.assign(param, assign_placeholder) self._cached_assign_ops[param] = assign_op self._cached_assign_placeholders[param] = assign_placeholder ops.append(self._cached_assign_ops[param]) feed_dict[self._cached_assign_placeholders[param]] = value.astype( dtype) if debug: print("setting value of %s" % param.name) tf.get_default_session().run(ops, feed_dict=feed_dict)
def set_param_values(self, flattened_params, all_params=False, **tags): debug = tags.pop("debug", False) # print("debug, all params", all_params) True # print("debug, param shapes", self.get_param_shapes(all_params, **tags)) #TODO: remove this hacky code param_values = unflatten_tensors( flattened_params, self.get_param_shapes(all_params, **tags)) ops = [] feed_dict = dict() for param, dtype, value in zip( self.get_params(all_params, **tags), self.get_param_dtypes(all_params, **tags), param_values): if param not in self._cached_assign_ops: assign_placeholder = tf.placeholder( dtype=param.dtype.base_dtype) assign_op = tf.assign(param, assign_placeholder) self._cached_assign_ops[param] = assign_op self._cached_assign_placeholders[param] = assign_placeholder ops.append(self._cached_assign_ops[param]) feed_dict[self._cached_assign_placeholders[param]] = value.astype( dtype) if debug: print("setting value of %s" % param.name) tf.get_default_session().run(ops, feed_dict=feed_dict)
def set_param_values_transfer(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors(flattened_params, self.get_param_shapes(**tags)) for param, dtype, value in zip(self.get_params(**tags), self.get_param_dtypes(**tags), param_values): if param.name != "output_log_std.param": param.set_value(value.astype(dtype))
def set_param_values(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors(flattened_params, self.get_param_shapes(**tags)) for param, dtype, value in zip(self.get_params(**tags), self.get_param_dtypes(**tags), param_values): param.set_value(value.astype(dtype)) if debug: print("setting value of %s" % param.name)
def set_param_values(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors( flattened_params, self.get_param_shapes(**tags)) for param, dtype, value in zip( self.get_params(**tags), self.get_param_dtypes(**tags), param_values): param.set_value(value.astype(dtype)) if debug: print("setting value of %s" % param.name)
def set_param_values(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors(flattened_params, self.get_param_shapes(**tags)) for param, dtype, value in zip(self.get_params(**tags), self.get_param_dtypes(**tags), param_values): if param.name == "leak_rate": value = np.minimum(1.0, np.maximum(0.0, value)) param.set_value(value.astype(dtype)) if debug: print("setting value of %s" % param.name)
def set_param_values(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors(flattened_params, self.get_param_shapes(**tags)) for param, dtype, value in zip(self.get_params(**tags), self.get_param_dtypes(**tags), param_values): if not self.reparam and param.name == "tc": if np.any(value < self.dt): print("Tc constraint violated:", self.dt, value) value = np.maximum(self.dt, value) param.set_value(value.astype(dtype)) if debug: print("setting value of %s" % param.name)
def set_param_values(self, flattened_params, **tags): debug = tags.pop("debug", False) param_values = unflatten_tensors( flattened_params, self.get_param_shapes(**tags)) ops = [] feed_dict = dict() for param, dtype, value in zip( self.get_params(**tags), self.get_param_dtypes(**tags), param_values): if param not in self._cached_assign_ops: assign_placeholder = tf.placeholder(dtype=param.dtype.base_dtype) assign_op = tf.assign(param, assign_placeholder) self._cached_assign_ops[param] = assign_op self._cached_assign_placeholders[param] = assign_placeholder ops.append(self._cached_assign_ops[param]) feed_dict[self._cached_assign_placeholders[param]] = value.astype(dtype) if debug: print("setting value of %s" % param.name) tf.get_default_session().run(ops, feed_dict=feed_dict)
def flat_to_params(self, flattened_params, all_params=False, **tags): return unflatten_tensors(flattened_params, self.get_param_shapes(all_params, **tags))
def flat_to_params(self, flattened_params, **tags): # Not used. import pdb pdb.set_trace() return unflatten_tensors(flattened_params, self.get_param_shapes(**tags))
def flat_to_params(self, flattened_params, **tags): return unflatten_tensors(flattened_params, self.get_param_shapes(**tags))
def flat_to_params(self, flattened_params, **tags): import numpy as np print(np.shape(flattened_params)) return unflatten_tensors(flattened_params, self.get_param_shapes(**tags))
def flat_to_params(self, flattened_params, **tags): if config.TF_NN_SETTRACE: ipdb.set_trace() return unflatten_tensors(flattened_params, self.get_param_shapes(**tags))