我是 Python 和机器学习的新手。我对 keras model.fiit 中的句子“validation_data will override validation_split”感到困惑。这是否意味着如果我像这样提供验证数据
history = model.fit(X_train, [train_labels_hotEncode,train_labels_hotEncode,train_labels_hotEncode],validation_data= (y_train,[test_labels_hotEncode,test_labels_hotEncode,test_labels_hotEncode]),train_labels_hotEncode]), validation_split=0.3 ,epochs=epochs, batch_size= 64, callbacks=[lr_sc])
验证分割不会被接受？而且该函数只会使用 Validation_data 而不是拆分？
此外，我正在尝试在 30% 的训练数据上测试我的数据。
但是，如果我尝试使用只有 validation_split = 0.3 的 model.fit，验证准确性就会变得非常糟糕。为此，我正在使用 inception googleNet 架构。
loss: 1.8204 - output_loss: 1.1435 - auxilliary_output_1_loss: 1.1292 - auxilliary_output_2_loss: 1.1272 - output_acc: 0.3845 - auxilliary_output_1_acc: 0.3797 - auxilliary_output_2_acc: 0.3824 - val_loss: 9.7972 - val_output_loss: 6.6655 - val_auxilliary_output_1_loss: 5.0973 - val_auxilliary_output_2_loss: 5.3417 - val_output_acc: 0.0000e+00 - val_auxilliary_output_1_acc: 0.0000e+00 - val_auxilliary_output_2_acc: 0.0000e+00
GOOGLENET 代码
input_layer = Input(shape=(224,224,3))
image = Conv2D(64,(7,7),padding='same', strides=(2,2), activation='relu', name='conv_1_7x7/2', kernel_initializer=kernel_init, bias_initializer=bias_init)(input_layer)
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_1_3x3/2')(image)
image = Conv2D(64, (1,1), padding='same', strides=(1,1), activation='relu', name='conv_2a_3x3/1' )(image)
image = Conv2D(192, (3,3), padding='same', strides=(1,1), activation='relu', name='conv_2b_3x3/1')(image)
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_2_3x3/2')(image)
image = inception_module(image,
filters_1x1= 64,
filters_3x3_reduce= 96,
filter_3x3 = 128,
filters_5x5_reduce=16,
filters_5x5= 32,
filters_pool_proj=32,
name='inception_3a')
image = inception_module(image,
filters_1x1=128,
filters_3x3_reduce=128,
filter_3x3=192,
filters_5x5_reduce=32,
filters_5x5=96,
filters_pool_proj=64,
name='inception_3b')
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_3_3x3/2')(image)
image = inception_module(image,
filters_1x1=192,
filters_3x3_reduce=96,
filter_3x3=208,
filters_5x5_reduce=16,
filters_5x5=48,
filters_pool_proj=64,
name='inception_4a')
image1 = AveragePooling2D((5,5), strides=3)(image)
image1 = Conv2D(128, (1,1), padding='same', activation='relu')(image1)
image1 = Flatten()(image1)
image1 = Dense(1024, activation='relu')(image1)
image1 = Dropout(0.4)(image1)
image1 = Dense(5, activation='softmax', name='auxilliary_output_1')(image1)
image = inception_module(image,
filters_1x1 = 160,
filters_3x3_reduce= 112,
filter_3x3= 224,
filters_5x5_reduce= 24,
filters_5x5= 64,
filters_pool_proj=64,
name='inception_4b')
image = inception_module(image,
filters_1x1= 128,
filters_3x3_reduce = 128,
filter_3x3= 256,
filters_5x5_reduce= 24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4c')
image = inception_module(image,
filters_1x1=112,
filters_3x3_reduce=144,
filter_3x3= 288,
filters_5x5_reduce= 32,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4d')
image2 = AveragePooling2D((5,5), strides=3)(image)
image2 = Conv2D(128, (1,1), padding='same', activation='relu')(image2)
image2 = Flatten()(image2)
image2 = Dense(1024, activation='relu')(image2)
image2 = Dropout(0.4)(image2) #Changed from 0.7
image2 = Dense(5, activation='softmax', name='auxilliary_output_2')(image2)
image = inception_module(image,
filters_1x1=256,
filters_3x3_reduce=160,
filter_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name= 'inception_4e')
image = MaxPool2D((3,3), padding='same', strides=(2,2), name='max_pool_4_3x3/2')(image)
image = inception_module(image,
filters_1x1=256,
filters_3x3_reduce=160,
filter_3x3= 320,
filters_5x5_reduce=32,
filters_5x5= 128,
filters_pool_proj=128,
name='inception_5a')
image = inception_module(image,
filters_1x1=384,
filters_3x3_reduce=192,
filter_3x3=384,
filters_5x5_reduce=48,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5b')
image = GlobalAveragePooling2D(name='avg_pool_5_3x3/1')(image)
image = Dropout(0.4)(image)
image = Dense(5, activation='softmax', name='output')(image)
model = Model(input_layer, [image,image1,image2], name='inception_v1')
model.summary()
epochs = 2
initial_lrate = 0.01 # Changed From 0.01
def decay(epoch, steps=100):
initial_lrate = 0.01
drop = 0.96
epochs_drop = 8
lrate = initial_lrate * math.pow(drop,math.floor((1+epoch)/epochs_drop))#
return lrate
sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
# nadam = keras.optimizers.Nadam(lr= 0.002, beta_1=0.9, beta_2=0.999, epsilon=None)
# keras
lr_sc = LearningRateScheduler(decay)
# rms = keras.optimizers.RMSprop(lr = initial_lrate, rho=0.9, epsilon=1e-08, decay=0.0)
# ad = keras.optimizers.adam(lr=initial_lrate)
model.compile(loss=['categorical_crossentropy', 'categorical_crossentropy','categorical_crossentropy'],loss_weights=[1,0.3,0.3], optimizer='sgd', metrics=['accuracy'])
# loss = 'categorical_crossentropy', 'categorical_crossentropy','categorical_crossentropy'
history = model.fit(X_train, [train_labels_hotEncode,train_labels_hotEncode,train_labels_hotEncode], validation_split=0.3 ,epochs=epochs, batch_size= 32, callbacks=[lr_sc])
谢谢，
最佳答案
validation_split 是传入的参数。它是一个数字，用于确定应如何将数据划分为训练集和验证集。例如，如果 validation_split = 0.1，则 10% 的数据将用于验证集，90% 的数据将用于测试集。
validation_data 是您显式传入验证集的参数。如果您传入验证数据，keras 会使用您显式传入的数据，而不是使用 validation_split 计算验证集。这就是“忽略”的含义 - 为 validation_data 传递的参数会覆盖为 validation_split 传递的任何参数。
在您的情况下，因为您想使用 30% 的数据作为验证数据，只需传入 validation_split=0.3 而不要传入 validation_data 的参数.
关于python - keras model.fit文档中 "validation_data will override validation_split."是什么意思，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54526575/