custom-training

问题 I'm trying to build a custom keras model whith the subclassing API but I get some errors when I load a previous instance of my model and I try to train it : Here is the class of my model, it has 3 inputs and 1 output : import tensorflow as tf spec1 = tf.TensorSpec(shape=(1,40,5,1)) spec2 = tf.TensorSpec(shape=(1,3)) class Conv_Rnn_model(tf.keras.Model): def __init__(self): # super() permet d'appeler le constructeur de la classe mère dans la classe fille # permet égalemet de résoudre le

问题 I'm trying to build a custom keras model whith the subclassing API but I get some errors when I load a previous instance of my model and I try to train it : Here is the class of my model, it has 3 inputs and 1 output : import tensorflow as tf spec1 = tf.TensorSpec(shape=(1,40,5,1)) spec2 = tf.TensorSpec(shape=(1,3)) class Conv_Rnn_model(tf.keras.Model): def __init__(self): # super() permet d'appeler le constructeur de la classe mère dans la classe fille # permet égalemet de résoudre le

问题 I'm trying to build a custom keras model whith the subclassing API but I get some errors when I load a previous instance of my model and I try to train it : Here is the class of my model, it has 3 inputs and 1 output : import tensorflow as tf spec1 = tf.TensorSpec(shape=(1,40,5,1)) spec2 = tf.TensorSpec(shape=(1,3)) class Conv_Rnn_model(tf.keras.Model): def __init__(self): # super() permet d'appeler le constructeur de la classe mère dans la classe fille # permet égalemet de résoudre le

问题 I'm trying to build a custom keras model whith the subclassing API but I get some errors when I load a previous instance of my model and I try to train it : Here is the class of my model, it has 3 inputs and 1 output : import tensorflow as tf spec1 = tf.TensorSpec(shape=(1,40,5,1)) spec2 = tf.TensorSpec(shape=(1,3)) class Conv_Rnn_model(tf.keras.Model): def __init__(self): # super() permet d'appeler le constructeur de la classe mère dans la classe fille # permet égalemet de résoudre le

问题 Here is the original script I'm trying to run on both CPU and GPU, I'm expecting a much faster training on GPU however it's taking almost the same time. I made the following modification to main() (the first 4 lines) because the original script does not activate / use the GPU. Suggestions ... ? def main(): physical_devices = tf.config.experimental.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) print('GPU activated

问题 Here is the original script I'm trying to run on both CPU and GPU, I'm expecting a much faster training on GPU however it's taking almost the same time. I made the following modification to main() (the first 4 lines) because the original script does not activate / use the GPU. Suggestions ... ? def main(): physical_devices = tf.config.experimental.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) print('GPU activated