ОШИБКА: недопустимая сериализация графика TensorFlow: ожидаемые входы NodeDef '' не соответствуют 1 указанному входу;Операционный выход: dtype;атр = значение: тензор;атр = DTYPE: тип>;NodeDef: dropout_1 / cond / dropout / random_uniform / max = Const_output_shapes = [[]], dtype = DT_FLOAT, value = Tensor
Я относительно новичок в мире TensorFlow.Когда я помещаю свою модель .pb в Android Studio, эта ошибка появляется в logcat.Другая модель .pb работает хорошо, поэтому я думаю, что есть проблема в обучающем коде.Это обучающий код:
from tensorflow.python.lib.io import file_io
import argparse
from pandas import read_csv
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, LSTM, Conv1D
from keras.callbacks import TensorBoard, ModelCheckpoint
from numpy import array, split
import keras.backend as k
import tensorflow as tf
from keras.models import load_model
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants
from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
# training parameters
epochs = 1
batch_size = 100
validation_split = 0.2
# model parameters
dropout = 0.2
timesteps = 40
timesteps_in_future = 20
nodes_per_layer = 32
filter_length = 3
def train_model(train_file='datanormalized.csv', job_dir='', **args):
parameter_string = '' + '_dropout_' + str(dropout) + '_timesteps_' + str(timesteps) + '_timesteps_in_future_' + str(timesteps_in_future) + '_nodes_per_layer_' + str(
nodes_per_layer) + '_filter_length_' + str(filter_length)
#load data
#file_stream = file.io.FileIO(train_file, mode='r')
file_stream = open(train_file,'r')
dataframe = read_csv(file_stream, header=0)
dataframe.fillna(0, inplace=True)
dataset= dataframe.values
X= dataset[:, [
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
]].astype(float)
print(X.shape)
y = dataset[:, 0] #ExerciseType è 0, ExerciseSubType è 1
#data parameters
data_dim = X.shape[1]
num_classes = len(set(y))
#scale x
scaler = MinMaxScaler(feature_range=(0,1))
X = scaler.fit_transform(X)
#encode Y
encoder = LabelEncoder()
encoder.fit(y)
encoded_y = encoder.transform(y)
hot_encoded_y = np_utils.to_categorical(encoded_y)
#prepare data for lstm
def create_LSTM_dataset(x, y, timesteps):
dataX, dataY = [],[]
for i in range(len(x) - timesteps + 1):
dataX.append(x[i:i + timesteps, :])
dataY.append(y[i + timesteps - timesteps_in_future - 1, :])
return array(dataX), array(dataY)
X, hot_encoded_y = create_LSTM_dataset(X, hot_encoded_y, timesteps)
#define model
model = Sequential([
Conv1D(nodes_per_layer, filter_length, strides=2, activation='relu', input_shape=(timesteps, data_dim), name='accelerations'),
Conv1D(nodes_per_layer, filter_length, strides=1, activation='relu'),
LSTM(nodes_per_layer, return_sequences=True),
LSTM(nodes_per_layer, return_sequences=False),
Dropout(dropout),
Dense(num_classes),
Activation('softmax', name='scores'),
])
model.summary()
#compile model
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
#define callbacks
callbacks = []
tensor_board = TensorBoard(log_dir='./test_logs', histogram_freq=1, write_graph=False, write_images=False)
callbacks.append(tensor_board)
checkpoint_path = 'best_weights.h5'
checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks.append(checkpoint)
#train model
model.fit(X, hot_encoded_y,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=validation_split,
callbacks=callbacks
)
print(model.output.op.name)
saver = tf.train.Saver()
saver.save(k.get_session(), '$(pwd)/keras_model.ckpt')
#load best checkpoint
model.load_weights('best_weights.h5')
#evaluate best model
def non_shuffling_train_test_split(X, y, test_size=validation_split):
i = int((1 - test_size) * X.shape[0]) + 1
X_train, X_test = split(X, [i])
y_train, y_test = split(y, [i])
return X_train, X_test, y_train, y_test
_, X_test, _, y_test = non_shuffling_train_test_split(X, hot_encoded_y, test_size=validation_split)
scores = model.evaluate(X_test, y_test, verbose=0)
acc = scores[1]
#save model
model_h5_name = 'model_acc_' + str(acc) + '.h5'
model.save(model_h5_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--train-file',
help='GCS or local paths to training data',
required=True
)
parser.add_argument(
'--job-dir',
help='GCS location to write checkpoints and export models',
required=True
)
args = parser.parse_args()
arguments = args.__dict__
train_model(**arguments)
Для модели требуется 12 входов, 3xГравитация, 3xAclelerations, шаг, крен, рыскание, 3xRotation.Модель предсказывает 4 типа упражнений: Break, Burpee, situp и Squat.
Я также пытался преобразовать его в Tensorflow lite, но дал мне другие ошибки.Может кто-нибудь помочь мне и сказать, где я не прав?Большое спасибо