input_shape=(64, 64, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(14)) model.add(Activation('soft...
I ran the simple mnist training following code with the different backends: Tensorflow, Pytorch and Jax. I get similar results with tensorflow and Jax: between 98 and 99% test accuracy but way lower results with Pytorch: below 90%. importosfromtimeimporttimeos.environ["KERAS_BACKEND"]="jax"...
def create_base_seqNetwork(input_dim): seq = Sequential() seq.add(Convolution2D(64, 11, 11, border_mode='same', trainable=True, init='he_normal', activation='relu', W_regularizer=l2(regularizer), subsample=(2, 2), input_shape=input_dim)) seq.add(MaxPooling2D(pool_size=(2, 2))...
model = VGG16(input_tensor=inputs,input_shape=input_shape,include_top=False) y = model.output y = layers.Dense(64, activation='relu')(y) y = layers.GlobalAveragePooling2D()(y) outputs = layers.Dense(num_classes, activation="softmax")(y) model =keras.Model(input...
Specifically, the stem is formed by three convolutional blocks with kernel size (1x5x5), (3x3x3) and (3x3x3), respectively. Each convolution op- erator is cascaded with a batch normalization (BN), ReLU and MaxPool. The pooling layer only halv...
However, unlike the original Xception network, we added an additional part at the end of the network with 256 filters, which does not include any max-pooling operations. While the depth-wise separable convolutions in Xception are more computationally efficient under resource constraints, we believe...
However, unlike the original Xception network, we added an additional part at the end of the network with 256 filters, which does not include any max-pooling operations. While the depth-wise separable convolutions in Xception are more computationally efficient under resource constraints, we believe...
validation, various architectural configurations, parameter combinations, and evaluation metrics were explored. The resulting optimal architecture consisted of a relatively shallow model with two narrow convolutional layers, followed by maxpooling layers, and a wide dense layer before generating the final ...
Moreover, we introduce difference convolution into temporal convolution for skeleton-based action recognition, which considers the discrepancy between the central joint and other joints within the temporal receptive field. Based on these modules, we proposed an adaptive multi-scale difference graph ...
Moreover, we introduce difference convolution into temporal convolution for skeleton-based action recognition, which considers the discrepancy between the central joint and other joints within the temporal receptive field. Based on these modules, we proposed an adaptive multi-scale difference graph ...