- 關於影像辨識，所有你應該知道的深度學習模型 | Medium @2018-02-04
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- Deep Learning in Computer Vision | Coursera
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How to Use the Keras Functional API for Deep Learning @2017-10-27
from keras.models import Model from keras.layers import Input from keras.layers import Dense visible = Input(shape=(10,)) hidden1 = Dense(10, activation='relu')(visible) hidden2 = Dense(20, activation='relu')(hidden1) hidden3 = Dense(10, activation='relu')(hidden2) output = Dense(1, activation='sigmoid')(hidden3) model = Model(inputs=visible, outputs=output) print(model.summary())
Layer (type) Output Shape Param # =========================================================================== input_1 (InputLayer) (None, 10) 0 ___________________________________________________________________________ dense_1 (Dense) (None, 10) 110 =(10+1)*10 ___________________________________________________________________________ dense_2 (Dense) (None, 20) 220 =(10+1)*20 ___________________________________________________________________________ dense_3 (Dense) (None, 10) 210 =(20+1)*10 ___________________________________________________________________________ dense_4 (Dense) (None, 1) 11 =(10+1)*1 =========================================================================== Total params: 551 Trainable params: 551 Non-trainable params: 0 -
How to calculate the number of parameters for convolutional neural network?
from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers.convolutional import Conv2D from keras.layers.pooling import MaxPooling2D visible = Input(shape=(64, 64, 1)) # fitler num: 32, filter shape: (4, 4) conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = Conv2D(16, kernel_size=4, activation='relu')(pool1) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) hidden1 = Dense(10, activation='relu')(pool2) output = Dense(1, activation='sigmoid')(hidden1) model = Model(inputs=visible, outputs=output) print(model.summary())
Layer (type) Output Shape Param # =========================================================================== input_1 (InputLayer) (None, 64, 64, 1) 0 ___________________________________________________________________________ # filter number 32, size 4*4. Say k=32, m=4, n=4, c=1 conv2d_1 (Conv2D) (None, 61, 61, 32) 544 = (4*4*1+1)*32 61=64-4+1 (m*n*c+1)*k ___________________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 30, 30, 32) 0 30=61/2 ___________________________________________________________________________ # filter number 16, size 4*4. Say k=16, m=4, n=4, c=32 conv2d_2 (Conv2D) (None, 27, 27, 16) 8208 = (4*4*32+1)*16 27=30-4+1 (m*n*c+1)*k ___________________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 13, 13, 16) 0 13=27/2 ___________________________________________________________________________ dense_1 (Dense) (None, 13, 13, 10) 170 = (16+1)*10 ___________________________________________________________________________ dense_2 (Dense) (None, 13, 13, 1) 11 =========================================================================== Total params: 8,933 Trainable params: 8,933 Non-trainable params: 0