keras-2

问题 I have been working on my project Deep Learning Language Detection which is a network with these layers to recognise from 16 programming languages: And this is the code to produce the network: # Setting up the model graph_in = Input(shape=(sequence_length, number_of_quantised_characters)) convs = [] for i in range(0, len(filter_sizes)): conv = Conv1D(filters=num_filters, kernel_size=filter_sizes[i], padding='valid', activation='relu', strides=1)(graph_in) pool = MaxPooling1D(pool_size=pooling

问题 I tried to implement a weighted binary crossentropy with Keras, but I am not sure if the code is correct. The training output seems to be a bit confusing. After a few epochs I just get an accuracy of ~0.15. I think thats much too less (even for a random guess). There are in general about 11% ones in the output and 89% zeros, therefore the weights are w_zero=0.89 and w_one=0.11. My code: def create_weighted_binary_crossentropy(zero_weight, one_weight): def weighted_binary_crossentropy(y_true,

问题 I tried to implement a weighted binary crossentropy with Keras, but I am not sure if the code is correct. The training output seems to be a bit confusing. After a few epochs I just get an accuracy of ~0.15. I think thats much too less (even for a random guess). There are in general about 11% ones in the output and 89% zeros, therefore the weights are w_zero=0.89 and w_one=0.11. My code: def create_weighted_binary_crossentropy(zero_weight, one_weight): def weighted_binary_crossentropy(y_true,

问题 I am doing some task related to image captioning and I have loaded the weights of inception model like this model = InceptionV3(weights='imagenet') But I am getting error like this: AttributeError: module 'tensorflow' has no attribute 'get_default_graph' What should I do? Please help. Here is the full output of above code. 1 . --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () 1 # Load the inception v3 model ---->

问题 I am doing some task related to image captioning and I have loaded the weights of inception model like this model = InceptionV3(weights='imagenet') But I am getting error like this: AttributeError: module 'tensorflow' has no attribute 'get_default_graph' What should I do? Please help. Here is the full output of above code. 1 . --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () 1 # Load the inception v3 model ---->

问题 Very briefly my question relates to image-size not remaining the same as the input image size after a maxpool layer when I use padding = 'same' in Keras code. I am going through the Keras blog: Building Autoencoders in Keras. I am building Convolution autoencoder. The autoencoder code is as follows: input_layer = Input(shape=(28, 28, 1)) x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_layer) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3), activation='relu',

问题 Very briefly my question relates to image-size not remaining the same as the input image size after a maxpool layer when I use padding = 'same' in Keras code. I am going through the Keras blog: Building Autoencoders in Keras. I am building Convolution autoencoder. The autoencoder code is as follows: input_layer = Input(shape=(28, 28, 1)) x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_layer) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(8, (3, 3), activation='relu',

问题 During some feature extraction experiments, I noticed that the 'model.pop()' functionality is not working as expected. For a pretrained model like vgg16, after using 'model.pop()' , model.summary() shows that the layer has been removed (expected 4096 features), however on passing an image through the new model, it results in the same number of features (1000) as the original model. No matter how many layers are removed including a completely empty model, it generates the same output. Looking

问题 During some feature extraction experiments, I noticed that the 'model.pop()' functionality is not working as expected. For a pretrained model like vgg16, after using 'model.pop()' , model.summary() shows that the layer has been removed (expected 4096 features), however on passing an image through the new model, it results in the same number of features (1000) as the original model. No matter how many layers are removed including a completely empty model, it generates the same output. Looking

来源： https://stackoverflow.com/questions/47297779/python-keras-an-layer-output-exactly-the-same-thing-as-input