tensorflow

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am

问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am

问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am

问题 I have a hdf5 training dataset with size (21760, 1, 33, 33) . 21760 is the whole number of training samples. I want to use the mini-batch training data with the size 128 to train the network. I want to ask: How to feed 128 mini-batch training data from the whole dataset with tensorflow each time? 回答1: You can read the hdf5 dataset into a numpy array, and feed slices of the numpy array to the TensorFlow model. Pseudo code like the following would work : import numpy, h5py f = h5py.File(

问题 Could anyone help with an an explanation of what axis is in TensorFlow 's one_hot function? According to the documentation: axis: The axis to fill (default: -1, a new inner-most axis) Closest I came to an answer on SO was an explanation relevant to Pandas: Not sure if the context is just as applicable. 回答1: Here's an example: x = tf.constant([0, 1, 2]) ... is the input tensor and N=4 (each index is transformed into 4D vector). axis=-1 Computing one_hot_1 = tf.one_hot(x, 4).eval() yields a (3,

问题 I followed Tensorflow guide to save my string data using: def _create_string_feature(values): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values.encode('utf-8')])) I also used ["tf.string", "FixedLenFeature"] as my feature original type, and "tf.string" as my feature convert type. However, during my training when I run my session and I create iterators, my string feature for a batch size of 2 (for example: ['food fruit', 'cupcake food' ]) would be like below. The problem is

问题 Example below works in 2.2; K.function is changed significantly in 2.3, now building a Model in Eager execution, so we're passing Model(inputs=[learning_phase,...]) . I do have a workaround in mind, but it's hackish, and lot more complex than K.function ; if none can show a simple approach, I'll post mine. from tensorflow.keras.layers import Input, Dense from tensorflow.keras.models import Model from tensorflow.python.keras import backend as K import numpy as np ipt = Input((16,)) x = Dense