def export_weights(self):
"""
Function to store TensorFlow weights back to into a dict in CoreML format to be used
by the C++ implementation
Returns
-------
tf_export_params: Dictionary
Dictionary of weights from TensorFlow stored as {weight_name: weight_value}
"""
tf_export_params = {}
with self.ac_graph.as_default():
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if 'weight' in var.name:
if var.name.startswith('conv'):
tf_export_params[var.name.split(
':')[0]] = _utils.convert_conv1d_tf_to_coreml(val)
elif var.name.startswith('dense'):
tf_export_params[var.name.split(
':')[0]] = _utils.convert_dense_tf_to_coreml(val)
elif var.name.startswith('rnn/lstm_cell/kernel'):
i2h_i, i2h_c, i2h_f, i2h_o, h2h_i, h2h_c, h2h_f, h2h_o = _utils.convert_lstm_weight_tf_to_coreml(
val, CONV_H)
tf_export_params['lstm_i2h_i_weight'] = i2h_i
tf_export_params['lstm_i2h_c_weight'] = i2h_c
tf_export_params['lstm_i2h_f_weight'] = i2h_f
tf_export_params['lstm_i2h_o_weight'] = i2h_o
tf_export_params['lstm_h2h_i_weight'] = h2h_i
tf_export_params['lstm_h2h_c_weight'] = h2h_c
tf_export_params['lstm_h2h_f_weight'] = h2h_f
tf_export_params['lstm_h2h_o_weight'] = h2h_o
elif var.name.startswith('rnn/lstm_cell/bias'):
h2h_i_bias, h2h_c_bias, h2h_f_bias, h2h_o_bias = _utils.convert_lstm_bias_tf_to_coreml(
val)
tf_export_params['lstm_h2h_i_bias'] = h2h_i_bias
tf_export_params['lstm_h2h_c_bias'] = h2h_c_bias
tf_export_params['lstm_h2h_f_bias'] = h2h_f_bias
tf_export_params['lstm_h2h_o_bias'] = h2h_o_bias
elif var.name.startswith('batch_normalization'):
tf_export_params[
'bn_' + var.name.split('/')[-1][0:-2]] = _np.array(val)
else:
tf_export_params[var.name.split(':')[0]] = _np.array(val)
tvars = _tf.global_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if 'moving_mean' in var.name:
tf_export_params['bn_running_mean'] = _np.array(val)
if 'moving_variance' in var.name:
tf_export_params['bn_running_var'] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(
tf_export_params[layer_name])
return tf_export_params
def export_weights(self):
tf_export_params = {}
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if 'weight' in var.name:
if 'conv' in var.name:
tf_export_params[var.name.split(':')[0]] = _utils.convert_conv2d_tf_to_coreml(val)
else:
tf_export_params[var.name.split(':')[0]] = _utils.convert_dense_tf_to_coreml(val)
else:
tf_export_params[var.name.split(':')[0]] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(tf_export_params[layer_name])
return tf_export_params
def export_weights(self):
_tf = _lazy_import_tensorflow()
tf_export_params = {}
with self.st_graph.as_default():
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if "weight" in var.name:
if "conv" in var.name:
tf_export_params[var.name.split(
":")[0]] = _utils.convert_conv2d_tf_to_coreml(val)
else:
tf_export_params[var.name.split(
":")[0]] = _utils.convert_dense_tf_to_coreml(val)
else:
tf_export_params[var.name.split(":")[0]] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(
tf_export_params[layer_name])
return tf_export_params
def export_weights(self):
"""
Function to store TensorFlow weights back to into a dict in CoreML format to be used
by the C++ implementation
Returns
-------
tf_export_params: Dictionary
Dictionary of weights from TensorFlow stored as {weight_name: weight_value}
"""
_tf = _lazy_import_tensorflow()
tf_export_params = {}
with self.ac_graph.as_default():
tvars = _tf.trainable_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if "weight" in var.name:
if var.name.startswith("conv"):
tf_export_params[var.name.split(
":")[0]] = _utils.convert_conv1d_tf_to_coreml(val)
elif var.name.startswith("dense"):
tf_export_params[var.name.split(
":")[0]] = _utils.convert_dense_tf_to_coreml(val)
elif var.name.startswith("rnn/lstm_cell/kernel"):
(
i2h_i,
i2h_c,
i2h_f,
i2h_o,
h2h_i,
h2h_c,
h2h_f,
h2h_o,
) = _utils.convert_lstm_weight_tf_to_coreml(val, CONV_H)
tf_export_params["lstm_i2h_i_weight"] = i2h_i
tf_export_params["lstm_i2h_c_weight"] = i2h_c
tf_export_params["lstm_i2h_f_weight"] = i2h_f
tf_export_params["lstm_i2h_o_weight"] = i2h_o
tf_export_params["lstm_h2h_i_weight"] = h2h_i
tf_export_params["lstm_h2h_c_weight"] = h2h_c
tf_export_params["lstm_h2h_f_weight"] = h2h_f
tf_export_params["lstm_h2h_o_weight"] = h2h_o
elif var.name.startswith("rnn/lstm_cell/bias"):
(
h2h_i_bias,
h2h_c_bias,
h2h_f_bias,
h2h_o_bias,
) = _utils.convert_lstm_bias_tf_to_coreml(val)
tf_export_params["lstm_h2h_i_bias"] = h2h_i_bias
tf_export_params["lstm_h2h_c_bias"] = h2h_c_bias
tf_export_params["lstm_h2h_f_bias"] = h2h_f_bias
tf_export_params["lstm_h2h_o_bias"] = h2h_o_bias
elif var.name.startswith("batch_normalization"):
tf_export_params[
"bn_" + var.name.split("/")[-1][0:-2]] = _np.array(val)
else:
tf_export_params[var.name.split(":")[0]] = _np.array(val)
tvars = _tf.global_variables()
tvars_vals = self.sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
if "moving_mean" in var.name:
tf_export_params["bn_running_mean"] = _np.array(val)
if "moving_variance" in var.name:
tf_export_params["bn_running_var"] = _np.array(val)
for layer_name in tf_export_params.keys():
tf_export_params[layer_name] = _np.ascontiguousarray(
tf_export_params[layer_name])
return tf_export_params
