tensorflow

问题 How can I fix error shown below? The input and output shape is supposed to be consist of 1 or -1. Here is my code: #Data Input main_input=Input(shape=(2*N_c),name='main_input') encoding_x=Dense(2*N_c,activation='relu',name='input_layer')(main_input) #Channel Input # channel_input=Input(shape=(4,),dtype='complex64',name='channel_input') channel_input = Lambda(set_channel2)(encoding_x) padded_channel = Lambda(z_padding,name='ppading_layerddddd')(channel_input) ffted_channel = Lambda(ffting,name

问题 How can I fix error shown below? The input and output shape is supposed to be consist of 1 or -1. Here is my code: #Data Input main_input=Input(shape=(2*N_c),name='main_input') encoding_x=Dense(2*N_c,activation='relu',name='input_layer')(main_input) #Channel Input # channel_input=Input(shape=(4,),dtype='complex64',name='channel_input') channel_input = Lambda(set_channel2)(encoding_x) padded_channel = Lambda(z_padding,name='ppading_layerddddd')(channel_input) ffted_channel = Lambda(ffting,name

问题 I need to freeze saved models for serving, but some saved model is device specific, how to solve this? with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: sess.run(tf.tables_initializer()) tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir) inference_graph_def=tf.get_default_graph().as_graph_def() for node in inference_graph_def.node: node.device = '' frozen_graph_path = os.path.join(frozen_dir, 'frozen_inference_graph.pb') output_keys = ['ToInt64

问题 I need to freeze saved models for serving, but some saved model is device specific, how to solve this? with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: sess.run(tf.tables_initializer()) tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir) inference_graph_def=tf.get_default_graph().as_graph_def() for node in inference_graph_def.node: node.device = '' frozen_graph_path = os.path.join(frozen_dir, 'frozen_inference_graph.pb') output_keys = ['ToInt64

问题 I need to freeze saved models for serving, but some saved model is device specific, how to solve this? with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: sess.run(tf.tables_initializer()) tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir) inference_graph_def=tf.get_default_graph().as_graph_def() for node in inference_graph_def.node: node.device = '' frozen_graph_path = os.path.join(frozen_dir, 'frozen_inference_graph.pb') output_keys = ['ToInt64

问题 I have a Seq2Seq model. I am interested to print out the matrix value of the output of the encoder per iteration. So for example as the dimension of the matrix in the encoder is (?,20) and the epoch =5 and in each epoch, there are 10 iteration, I would like to see 10 matrix of the dimension (?,20) per epoch . I have gone to several links as here but it still does not print out the value matrix. With this code as mentioned in the aboved link: import keras.backend as K k_value = K.print_tensor

问题 I have a Keras Sequential model taking inputs from csv files. When I run the model, its accuracy remains zero even after 20 epochs. I have gone through these two stackoverflow threads (zero-accuracy-training and why-is-the-accuracy-for-my-keras-model-always-0) but nothing solved my problem. As my model is binary classification, and I think it should not work like a regression model to make accuracy metric ineffective. Here is the Model def preprocess(*fields): return tf.stack(fields[:-1]), tf

问题 The Keras website has this article about exporting Keras models to core Tensorflow. However the step new_model = model_from_config(config) throws an error: Traceback (most recent call last): File "/home/hal9000/tf_serving_experiments/sndbx.py", line 38, in <module> new_model = model_from_config(config) File "/home/hal9000/keras2env/local/lib/python2.7/site-packages/keras/models.py", line 304, in model_from_config return layer_module.deserialize(config, custom_objects=custom_objects) File "

问题 I recently was reading a Pytorch code and came across loss.backward() and optimizer.step() functions, are there any equivalent of these using tensorflow/keras? 回答1: loss.backward() equivalent in tensorflow is tf.GradientTape() . TensorFlow provides the tf.GradientTape API for automatic differentiation - computing the gradient of a computation with respect to its input variables. Tensorflow "records" all operations executed inside the context of a tf.GradientTape onto a "tape". Tensorflow then

问题 I recently was reading a Pytorch code and came across loss.backward() and optimizer.step() functions, are there any equivalent of these using tensorflow/keras? 回答1: loss.backward() equivalent in tensorflow is tf.GradientTape() . TensorFlow provides the tf.GradientTape API for automatic differentiation - computing the gradient of a computation with respect to its input variables. Tensorflow "records" all operations executed inside the context of a tf.GradientTape onto a "tape". Tensorflow then