我需要在 Keras 中构建一个与 tensorflow 1.15 兼容的 LSTM 网络。
这是我打开的另一篇关于如何创建网络的帖子。 keras LSTM 模型 - 与 tflite 一起使用的 tf 1.15 等效模型
我正在使用的版本：
tf版本：1.15.0，tf.keras版本：2.2.4-tf
按照这个例子： https ://github.com/tensorflow/tensorflow/tree/r1.15/tensorflow/lite/experimental/examples/lstm https://github.com/tensorflow/tensorflow/blob/r1.15/tensorflow /lite/experimental/examples/lstm/TensorFlowLite_LSTM_Keras_Tutorial.ipynb
我设法为我需要的网络创建了一个代码。
这是“源”网络（带有 keras.LSTM 层）：
inputs = keras.Input(shape=(1, 52))
state_1_h = keras.Input(shape=(200,))
state_1_c = keras.Input(shape=(200,))
x1, state_1_h_out, state_1_c_out = layers.LSTM(200, return_sequences=True, input_shape=(sequence_length, 52),
return_state=True)(inputs, initial_state=[state_1_h, state_1_c])
output = layers.Dense(13)(x1)
model = keras.Model([inputs, state_1_h, state_1_c],
[output, state_1_h_out, state_1_c_out])
这就是我实现它的方式：
os.environ['TF_ENABLE_CONTROL_FLOW_V2'] = '1'
from tensorflow.keras import Model
import tensorflow as tf
print(f"tf version: {tf.__version__}, tf.keras version: {tf.keras.__version__}")
from tensorflow.keras.utils import plot_model
def buildLstmLayer(merged_inputs, num_units):
inputs = merged_inputs[0]
state_1_h_keras = merged_inputs[1]
state_1_c_keras = merged_inputs[2]
initial_state = tf.nn.rnn_cell.LSTMStateTuple(state_1_h_keras, state_1_c_keras)
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units, state_is_tuple=True)
outputs, out_states = tf.lite.experimental.nn.dynamic_rnn(
cell,
inputs,
dtype='float32',
time_major=True,
initial_state=initial_state)
state_1_h_out, state_1_c_out = out_states
state_1_h_out_keras = tf.keras.Input(tensor=(state_1_h_out), name='state_1_h_out')
state_1_c_out_keras = tf.keras.Input(tensor=(state_1_c_out), name='state_1_c_out')
return outputs, state_1_h_out_keras, state_1_c_out_keras
tf.reset_default_graph()
inputs = tf.keras.layers.Input(shape=(1, 52), name='input')
batch_size = tf.shape(inputs)[1]
cell = tf.nn.rnn_cell.BasicLSTMCell(200, state_is_tuple=True)
initial_state = cell.zero_state(batch_size, tf.float32)
state_1_h, state_1_c = initial_state
state_1_h_keras = tf.keras.Input(tensor=(state_1_h), name='state_1_h')
state_1_c_keras = tf.keras.Input(tensor=(state_1_c), name='state_1_c')
x1, state_1_h_out_keras, state_1_c_out_keras = tf.keras.layers.Lambda(buildLstmLayer, arguments={'num_units': 200})([inputs, state_1_h_keras, state_1_c_keras])
output = tf.keras.layers.Dense(13, activation=tf.nn.softmax, name='output')(x1)
model = Model([inputs, state_1_h_keras, state_1_c_keras],
[output, state_1_h_out_keras, state_1_c_out_keras])
现在，我需要将其转换为 TFLite。
首先，当我尝试保存并加载它时，它不起作用：
model.save("model_working.h5")
loaded_model = load_model("model_working.h5")
在这个笔记本中，他们展示了如何正确地将其转换为 TFLite。
# Step 3: Convert the Keras model to TensorFlow Lite model.
sess = tf.keras.backend.get_session()
input_tensor = sess.graph.get_tensor_by_name('input:0')
output_tensor = sess.graph.get_tensor_by_name('output/Softmax:0')
converter = tf.lite.TFLiteConverter.from_session(
sess, [input_tensor], [output_tensor])
tflite = converter.convert()
我尝试在我的代码中遵循相同的模式：
sess = tf.keras.backend.get_session()
inputs_tensors = [sess.graph.get_tensor_by_name(tensor_name) for tensor_name in [x.name for x in model.inputs]]
outputs_tensors = [sess.graph.get_tensor_by_name(tensor_name) for tensor_name in [x.name for x in model.outputs]]
converter = tf.lite.TFLiteConverter.from_session(
sess, inputs_tensors, outputs_tensors)
tflite = converter.convert()
但我收到：
2021-02-06 21:24:44.536897: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2021-02-06 21:24:44.550694: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f944418c2f0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-02-06 21:24:44.550705: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:From /Users/yonatab/PycharmProjects/LSTM_in_android/model_working_for_issue_convert.py:50: The name tf.keras.backend.get_session is deprecated. Please use tf.compat.v1.keras.backend.get_session instead.
2021-02-06 21:24:44.678534: I tensorflow/core/grappler/devices.cc:60] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA support)
2021-02-06 21:24:44.678584: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2021-02-06 21:24:44.702930: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:786] Optimization results for grappler item: graph_to_optimize
2021-02-06 21:24:44.702943: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: Graph size after: 514 nodes (0), 626 edges (0), time = 5.445ms.
2021-02-06 21:24:44.702946: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: Graph size after: 514 nodes (0), 626 edges (0), time = 6.09ms.
2021-02-06 21:24:44.702948: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:786] Optimization results for grappler item: lambda_rnn_while_cond_49
2021-02-06 21:24:44.702951: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: function_optimizer did nothing. time = 0.001ms.
2021-02-06 21:24:44.702953: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: function_optimizer did nothing. time = 0ms.
2021-02-06 21:24:44.702955: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:786] Optimization results for grappler item: lambda_rnn_while_body_50
2021-02-06 21:24:44.702958: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: function_optimizer did nothing. time = 0.001ms.
2021-02-06 21:24:44.702960: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788] function_optimizer: function_optimizer did nothing. time = 0ms.
WARNING:tensorflow:From /Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/util.py:208: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.convert_variables_to_constants`
WARNING:tensorflow:From /Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
Traceback (most recent call last):
File "/Users/yonatab/PycharmProjects/LSTM_in_android/model_working_for_issue_convert.py", line 55, in <module>
sess, inputs_tensors, outputs_tensors)
File "/Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/lite.py", line 628, in from_session
graph_def = _freeze_graph(sess, input_tensors, output_tensors)
File "/Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/util.py", line 244, in freeze_graph
hinted_outputs_nodes)
File "/Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/util.py", line 209, in _convert_op_hints_if_present
graph_def = convert_op_hints_to_stubs(graph_def=graph_def)
File "/Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/op_hint.py", line 1288, in convert_op_hints_to_stubs
return _convert_op_hints_to_stubs_helper(graph_def, write_callback)
File "/Users/yonatab/PycharmProjects/LSTM_in_android/venv_tf_115/lib/python3.7/site-packages/tensorflow_core/lite/python/op_hint.py", line 1179, in _convert_op_hints_to_stubs_helper
assert (len(children_hints) > 0) # pylint: disable=g-explicit-length-test
AssertionError
试试 2 -
如果我使用
cell = tf.lite.experimental.nn.TFLiteLSTMCell(num_units, state_is_tuple=True)
代替
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units, state_is_tuple=True)
我收到：
tensorflow.lite.python.convert.ConverterError: See console for info.
2021-02-08 15:41:35.471411: F tensorflow/lite/toco/tooling_util.cc:935] Check failed: GetOpWithOutput(model, output_array) Specified output array "lambda/rnn/while/Identity_4" is not produced by any op in this graph. Is it a typo? This should not happen. If you trigger this error please send a bug report (with code to reporduce this error), to the TensorFlow Lite team.
Fatal Python error: Aborted
我错过了什么？
它与此文档说明有关吗？
谢谢