neural-network

问题 I am trying to code and learn different neural network models. I am having a lot of complication with input dimensionality. I am looking for some tutorial that shows differences in layers and how to set input and outputs for each layers. 回答1: Keras documentation shows you all the input_shape s expected by each layer. In Keras, you'll see input shapes in these forms: input_shape defined by user in layers shapes shown in summaries and others array shapes tensor shapes Input shape defined by

问题 I've trained the following model for some timeseries in Keras: input_layer = Input(batch_shape=(56, 3864)) first_layer = Dense(24, input_dim=28, activation='relu', activity_regularizer=None, kernel_regularizer=None)(input_layer) first_layer = Dropout(0.3)(first_layer) second_layer = Dense(12, activation='relu')(first_layer) second_layer = Dropout(0.3)(second_layer) out = Dense(56)(second_layer) model_1 = Model(input_layer, out) Then I defined a new model with the trained layers of model_1 and

问题 I am new to Keras and I am trying to get the weights in Keras. I know how to do it in Tensorflow in Python. Code: data = np.array(attributes, 'int64') target = np.array(labels, 'int64') feature_columns = [tf.contrib.layers.real_valued_column("", dimension=2, dtype=tf.float32)] learningRate = 0.1 epoch = 10000 # https://www.tensorflow.org/api_docs/python/tf/metrics validation_metrics = { "accuracy": tf.contrib.learn.MetricSpec(metric_fn = tf.contrib.metrics.streaming_accuracy , prediction_key

问题 I have a little comprehension problem with CNN. And I'm not quite sure how many filters and thus weights are trained. Example: I have an input layer with the 32x32 pixels and 3 channels (i.e. shape of (32,32,3) ). Now I use a 2D-convolution layer with 10 filters of shape (4,4) . So I end up with 10 channels each with shape of (28,28) , but do I now train a separate filter for each input channel or are they shared? Do I train 3x10x4x4 weights or do I train 10x4x4 weights? 回答1: You can find out

问题 I have written the following binary classification program in tensorflow that is buggy. The cost is returning to be zero all the time no matter what the input is. I am trying to debug a larger program which is not learning anything from the data. I have narrowed down at least one bug to the cost function always returning zero. The given program is using some random inputs and is having the same problem. self.X_train and self.y_train is originally supposed to read from files and the function

问题 I have a network that produces a 4D output tensor where the value at each position in spatial dimensions (~pixel) is to be interpreted as the class probabilities for that position. In other words, the output is (num_batches, height, width, num_classes) . I have labels of the same size where the real class is coded as one-hot. I would like to calculate the categorical-crossentropy loss using this. Problem #1: The K.softmax function expects a 2D tensor (num_batches, num_classes) Problem #2 : I

问题 Given a trained keras model I am trying to compute the gradient of the output with respect to the input. This example tries to fit the function y=x^2 with a keras model composed by 4 layers of relu activations, and compute the gradient of the model output with respect to the input. from keras.models import Sequential from keras.layers import Dense from keras import backend as k from sklearn.model_selection import train_test_split import numpy as np import tensorflow as tf # random data x = np

问题 Is it possible to get the file names that were loaded using flow_from_directory ? I have : datagen = ImageDataGenerator( rotation_range=3, # featurewise_std_normalization=True, fill_mode='nearest', width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True ) train_generator = datagen.flow_from_directory( path+'/train', target_size=(224, 224), batch_size=batch_size,) I have a custom generator for my multi output model like: a = np.arange(8).reshape(2, 4) # print(a) print(train

问题 I have been experimenting with neural networks these days. I have come across a general question regarding the activation function to use. This might be a well known fact to but I couldn't understand properly. A lot of the examples and papers I have seen are working on classification problems and they either use sigmoid (in binary case) or softmax (in multi-class case) as the activation function in the out put layer and it makes sense. But I haven't seen any activation function used in the

问题 I am trying to make a simple proof-of-concept where I can see the probabilities of different classes for a given prediction. However, everything I try seems to only output the predicted class, even though I am using a softmax activation. I am new to machine learning, so I'm not sure if I am making a simple mistake or if this is a feature not available in Keras. I'm using Keras + TensorFlow. I have adapted one of the basic examples given by Keras for classifying the MNIST dataset. My code