keras

问题 As much as I know that Triplet Loss is a Loss Function which decrease the distance between anchor and positive but decrease between anchor and negative. Also, there is a margin added to it. So for EXAMPLE LEt us Suppose: a Siamese Network , which gives embeddings: anchor_output = [1,2,3,4,5...] # embedding given by the CNN model positive_output = [1,2,3,4,4...] negative_output= [53,43,33,23,13...] And I think I can get the triplet loss such as: (I think I have to make it as loss using Lambda

问题 model.fit throws an error ValueError: Error when checking input: expected embedding_1_input to have shape (32,) but got array with shape (1,) , but there are no arrays of shape (1,) passed to model.fit . def create_model(vocabulary_size, input_word_count, embedding_dims=50): model = Sequential() model.add(Embedding(vocabulary_size, embedding_dims, input_length=input_word_count)) model.add(GlobalAveragePooling1D()) model.add(Dense(1, activation="sigmoid")) model.compile(loss="binary

问题 I am trying to train an LSTM recurrent neural network, for sequence classification. My data has the following formart: Input: [1,5,2,3,6,2, ...] -> Output: 1 Input: [2,10,4,6,12,4, ...] -> Output: 1 Input: [4,1,7,1,9,2, ...] -> Output: 2 Input: [1,3,5,9,10,20, ...] -> Output: 3 . . . So basically I want to provide a sequence as an input and get an integer as an output. Each input sequence has length = 2000 float numbers, and I have around 1485 samples for training The output is just an

问题 In the first chapter of Deep Learning with TensorFlow , it gives an example on how to build a simple neural network for recognizing handwritten digits. According to its description, the code bundle for the book can be found at GitHub. From the context, I think section Running a simple TensorFlow 2.0 net and establishing a baseline uses the code same with Deep-Learning-with-TensorFlow-2-and-Keras/mnist_V1.py. When I run this example code, it gives me the following output: The snapshot from the

问题 Thanks to this great article(http://machinethink.net/blog/coreml-custom-layers/), I understood how to write converting using coremltools and Lambda with Keras custom layer. But, I cannot understand on the situation, function with two parameters. #python def scaling(x, scale): return x * scale Keras layer is here. #python up = conv2d_bn(mixed, K.int_shape(x)[channel_axis], 1, activation=None, use_bias=True, name=name_fmt('Conv2d_1x1')) x = Lambda(scaling, # HERE !! output_shape=K.int_shape(up)

问题 I'm working on a neural network which approximates a function f(X)=y, with X a vector [x0, .., xn] and y in [-inf, +inf]. This approximated function needs to have an accuracy (sum of errors) around 1e-8. In fact, I need my neural network to overfit. X is composed of random points in the interval -500 and 500. Before putting these points into the input layer I normalized them between [0, 1]. I use keras as follow: dimension = 10 #example self.model = Sequential() self.model.add(Dense(128,

问题 Can someone point me to a complete example that does all of the following? Fits batched (and pickled) data in a loop using train_on_batch() Sets aside data from each batch for validation purposes Sets aside test data for accuracy evaluation after all batches have been processed (see last line of my example below). I'm finding lots of 1 - 5 line code snippets on the internet illustrating how to call train_on_batch() or fit_generator() , but so far nothing that clearly illustrates how to

问题 I am experimenting with LSTMs in Keras with little to no luck. At some moment I decided to scale back to the most basic problems in order finally achieve some positive result. However, even with simplest problems I find that Keras is unable to converge while the implementation of the same problem in Tensorflow gives stable result. I am unwilling to just switch to Tensorflow without understanding why Keras keeps diverging on any problem I attempt. My problem is a many-to-many sequence

问题 I'm creating an Image Classifier using Tensorflow and Keras, but when I tried to train my model I got an error: IndexError: list index out of range. I think the problem is with my model, because when I remove the conv2D layers, then the code throws no error. model = Sequential() model.add(Conv2D(64,(3,3),activation='relu',padding='same')) model.add(Conv2D(64,(3,3),activation='relu',padding='same')) model.add(MaxPool2D((2,2),strides=(2,2))) model.add(Conv2D(128,(3,3),activation='relu',padding=

问题 I was trying to train my siamese network with fit_generator() ,I learned from this answer: Keras: How to use fit_generator with multiple inputs that the best way to do this was to create your own generator that yield the multiple data points, my problem was that I retrieve my data with flow_from_directory() function and I didn't know if that was possible. This is my attempt to readapt a generator for my problem: from keras.models import load_model from keras import optimizers from keras