I have a network, I want to apply softmax on dense layer. I have dense layer of shape (?, 312), I want to apply softmax on dense layer on units 1-9, 10-18...etc. I don't know how to do that. I mentioned an image below, I want something like this.
我尝试了类似的方法，但这对我来说似乎并不正确，因为我正在采用整个 312 个单位并将其转换为另一个密集层。我想直接在312单位申请。
from keras.layers import Input, Dense, Conv2D, BatchNormalization, Activation, MaxPooling2D, Dropout, Flatten
from keras.models import Model
from keras.layers import concatenate
class CubNet:
@staticmethod
def main_network(inputs,chanDim):
inputs = inputs
x = Conv2D(32, (3, 3), padding="same")(inputs)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(3, 3))(x)
x = Dropout(0.25)(x)
#(CONV => RELU) * 2 => POOL
x = Conv2D(64, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2D(64, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
# (CONV => RELU) * 2 => POOL
x = Conv2D(128, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2D(128, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
return x
@staticmethod
def category_branch(inputs,numCategories,chanDim):
inputs = inputs
x = Conv2D(128, (3, 3), padding="same")(inputs)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(3, 3))(x)
x = Dropout(0.25)(x)
# (CONV => RELU) * 2 => POOL
x = Conv2D(128, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = Conv2D(128, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
x = Dense(1024)(x)
x = Activation("relu")(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
# softmax classifier
x = Dense(numCategories)(x)
x = Activation("softmax", name = "category_output")(x)
# return the constructed network architecture
return x
def Attribute_branch(inputs, numAtt, chanDim):
inputs = inputs
x = Conv2D(16, (3, 3), padding="same")(inputs)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(3, 3))(x)
x = Dropout(0.25)(x)
# CONV => RELU => POOL
x = Conv2D(32, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
# CONV => RELU => POOL
x = Conv2D(32, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
# define a branch of output layers for the number of different
# colors (i.e., red, black, blue, etc.)
x = Flatten()(x)
x = Dense(312)(x)
x = Activation("relu")(x)
x = BatchNormalization()(x)
print(x.shape)
x1 = Dense(9)(x)
x2 = Dense(15)(x)
x3 = Dense(15)(x)
x4 = Dense(15)(x)
x5 = Dense(4)(x)
x6 = Dense(15)(x)
x7 = Dense(6)(x)
x8 = Dense(15)(x)
x9 = Dense(11)(x)
x10 = Dense(15)(x)
x11 = Dense(15)(x)
x12 = Dense(14)(x)
x13 = Dense(3)(x)
x14 = Dense(15)(x)
x15 = Dense(15)(x)
x16 = Dense(15)(x)
x17 = Dense(15)(x)
x18 = Dense(5)(x)
x19 = Dense(5)(x)
x20 = Dense(14)(x)
x21 = Dense(4)(x)
x22 = Dense(4)(x)
x23 = Dense(4)(x)
x24 = Dense(15)(x)
x25 = Dense(15)(x)
x26 = Dense(15)(x)
x27 = Dense(15)(x)
#x28 = Dense(4)(x)
x1 = Activation("softmax", name = "has_bill_shape")(x1)
x2 = Activation("softmax", name = "has_wing_color")(x2)
x3 = Activation("softmax", name = "has_upperpart_color")(x3)
x4 = Activation("softmax", name = "has_underpart_color")(x4)
x5 = Activation("softmax", name = "has_breast_pattern")(x5)
x6 = Activation("softmax", name = "has_back_color")(x6)
x7 = Activation("softmax", name = "has_tail_shape")(x7)
x8 = Activation("softmax", name = "has_uppertail_color")(x8)
x9 = Activation("softmax", name = "has_head_pattern")(x9)
x10 = Activation("softmax", name = "has_breast_color")(x10)
x11 = Activation("softmax", name = "has_throat_color")(x11)
x12 = Activation("softmax", name = "has_eye_color")(x12)
x13 = Activation("softmax", name = "has_bill_length")(x13)
x14 = Activation("softmax", name = "has_forhead_color")(x14)
x15 = Activation("softmax", name = "has_undertail_color")(x15)
x16 = Activation("softmax", name = "has_nape_color")(x16)
x17 = Activation("softmax", name = "has_belly_color")(x17)
x18 = Activation("softmax", name = "has_wing_shape")(x18)
x19 = Activation("softmax", name = "has_size")(x19)
x20 = Activation("softmax", name = "has_shape")(x20)
x21 = Activation("softmax", name = "has_back_pattern")(x21)
x22 = Activation("softmax", name = "has_tail_pattern")(x22)
x23 = Activation("softmax", name = "has_belly_pattrern")(x23)
x24 = Activation("softmax", name = "has_primary_color")(x24)
x25 = Activation("softmax", name = "has_leg_color")(x25)
x26 = Activation("softmax", name = "has_bill_color")(x26)
x27 = Activation("softmax", name = "has_crown_color")(x27)
#x28 = Activation("softmax", name = "has_wing_pattern")(x28)
x = concatenate([x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21,x21,x22,x23
,x24,x25,x26,x27], name="Attribute_output" )
#print(x.shape)
return x
@staticmethod
def Build(numCategories, numAttributes, finalAct="softmax"):
inputshape = (299,299,3)
chanDim = -1
inputs = Input(shape=inputshape)
main_output = CubNet.main_network(inputs, chanDim = chanDim)
categoryBranch = CubNet.category_branch(main_output,numCategories, chanDim=chanDim)
AttBranch = CubNet.Attribute_branch(main_output, numAttributes, chanDim=chanDim)
model = Model(inputs=inputs, outputs=[categoryBranch, AttBranch], name ='Cub')
return model
model = CubNet.Build(numCategories=200, numAttributes=312, finalAct="softmax")
losses = {"category_output" : "categorical_crossentropy",
"Attribute_output" : "binary_crossentropy"}
loss_weight = {"category_output" : 1.0,
"Attribute_output" : 1.0}
print("[INFO] Compiling Model....")
opt = Adam(lr = INIT_LR, decay = INIT_LR/EPOCHS)
model.compile(optimizer=opt, loss=losses, loss_weights=loss_weight, metrics=["accuracy"])
H = model.fit(trainX, {"category_output": trainCategoryY, "Attribute_output": trainAttY},
validation_data = (valX,{"category_output": valCategoryY, "Attribute_output": valAttY}),
epochs= EPOCHS, verbose=1)
print("[INFO] serializing network....")
model.save("ATT_categorical.h5")
希望有人解答。Here https://datascience.stackexchange.com/questions/23614/keras-multiple-softmax-in-last-layer-possible是同一问题的链接，但这不起作用，因为密集层不接受 2 个参数。
我认为最简单的方法是使用Reshape层，然后沿正确的轴应用 softmax：
from keras.layers import Input, Lambda, Reshape
from keras.models import Model
from keras.activations import softmax
import numpy as np
inp = Input(shape=(312,1))
x = Reshape((78,4,1))(inp)
out = Lambda(lambda x: softmax(x, axis=2))(x)
model = Model(inp, out)
output = model.predict(np.zeros((1,312,1)))
请注意，Reshape 不需要批量大小。