"Fine-tune InceptionV3/ResNet50 on a new set of classes" doesn't work, while VGG16 works (suspect BN) #9214
Description
The following code works as expected with vgg16 (no BN) but not with resnet50 or inception_v3 (BN). My hypothesis is that it's due to BN. The code follows "Fine-tune InceptionV3 on a new set of classes" from https://keras.io/applications/#usage-examples-for-image-classification-models
from keras.preprocessing import image
from keras.applications import resnet50, inception_v3, vgg16
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D, Input
from keras.optimizers import Adam
import numpy as np
batch_size = 50
num_classes = 2
#base_model = resnet50.ResNet50
#base_model = inception_v3.InceptionV3
base_model = vgg16.VGG16
base_model = base_model(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(loss='sparse_categorical_crossentropy',
optimizer=Adam(lr=0.0001),
metrics=['acc'])
x_train = np.random.normal(loc=127, scale=127, size=(50, 224,224,3))
y_train = np.array([0,1]*25)
x_train = resnet50.preprocess_input(x_train)
print(model.evaluate(x_train, y_train, batch_size=batch_size, verbose=0))
model.fit(x_train, y_train,
epochs=100,
batch_size=batch_size,
shuffle=False,
validation_data=(x_train, y_train))