keras-layer

In Keras, I'm trying to import _obtain_input_shape as follows: from keras.applications.imagenet_utils import _obtain_input_shape However, I get the following error: ImportError: cannot import name '_obtain_input_shape' The reason I'm trying to import _obtain_input_shape is so that I can determine the input shape(so as to load VGG-Face as follows : I'm using it to determine the correct input shape of the input tensor as follow: input_shape = _obtain_input_shape(input_shape, default_size=224, min_size=48, data_format=K.image_data_format(), require_flatten=include_top)` Please assist? Thanks in

In my previous question , I used Keras' Layer.set_input() to connect my Tensorflow pre-processing output tensor to my Keras model's input. However, this method has been removed after Keras version 1.1.1 . How can I achieve this in newer Keras versions? Example: # Tensorflow pre-processing raw_input = tf.placeholder(tf.string) ### some TF operations on raw_input ### tf_embedding_input = ... # pre-processing output tensor # Keras model model = Sequential() e = Embedding(max_features, 128, input_length=maxlen) ### THIS DOESN'T WORK ANYMORE ### e.set_input(tf_embedding_input) #####################

Using ResNet50 pre-trained Weights I am trying to build a classifier. The code base is fully implemented in Keras high-level Tensorflow API. The complete code is posted in the below GitHub Link. Source Code: Classification Using RestNet50 Architecture The file size of the pre-trained model is 94.7mb . I loaded the pre-trained file new_model = Sequential() new_model.add(ResNet50(include_top=False, pooling='avg', weights=resnet_weight_paths)) and fit the model train_generator = data_generator.flow_from_directory( 'path_to_the_training_set', target_size = (IMG_SIZE,IMG_SIZE), batch_size = 12,

I want to define lambda layer to combine features with cross product, then merge those models,just like the fig. ,What should I do? Test model_1, get 128 dimensions form dense, use pywt get two 64 dimensions feature( cA,cD ), then return cA*cD //of course I want to combine two models ,but try model_1 first. from keras.models import Sequential,Model from keras.layers import Input,Convolution2D,MaxPooling2D from keras.layers.core import Dense,Dropout,Activation,Flatten,Lambda import pywt def myFunc(x): (cA, cD) = pywt.dwt(x, 'db1') # x=x*x return cA*cD batch_size=32 nb_classes=3 nb_epoch=20 img

I got this error message when declaring the input layer in Keras. ValueError: Negative dimension size caused by subtracting 3 from 1 for 'conv2d_2/convolution' (op: 'Conv2D') with input shapes: [?,1,28,28], [3,3,28,32]. My code is like this model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28))) Sample application: https://github.com/IntellijSys/tensorflow/blob/master/Keras.ipynb By default, Convolution2D ( https://keras.io/layers/convolutional/ ) expects the input to be in the format (samples, rows, cols, channels), which is "channels-last". Your data seems to be in the

I've been trying to build a sequential model in Keras using the pooling layer tf.nn.fractional_max_pool . I know I could try making my own custom layer in Keras, but I'm trying to see if I can use the layer already in Tensorflow. For the following code snippet: p_ratio=[1.0, 1.44, 1.44, 1.0] model = Sequential() model.add(ZeroPadding2D((2,2), input_shape=(1, 48, 48))) model.add(Conv2D(320, (3, 3), activation=PReLU())) model.add(ZeroPadding2D((1,1))) model.add(Conv2D(320, (3, 3), activation=PReLU())) model.add(InputLayer(input_tensor=tf.nn.fractional_max_pool(model.layers[3].output, p_ratio)))

I'm building a model that converts a string to another string using recurrent layers (GRUs). I have tried both a Dense and a TimeDistributed(Dense) layer as the last-but-one layer, but I don't understand the difference between the two when using return_sequences=True, especially as they seem to have the same number of parameters. My simplified model is the following: InputSize = 15 MaxLen = 64 HiddenSize = 16 inputs = keras.layers.Input(shape=(MaxLen, InputSize)) x = keras.layers.recurrent.GRU(HiddenSize, return_sequences=True)(inputs) x = keras.layers.TimeDistributed(keras.layers.Dense