SqueezeNet training accuracy stuck at 0.37 for face recognition task

[madhurkashyap]

Tensorflow Version

1.6

Keras Version

2.1

Keras-squeezenet Version

Latest

Bug reports:

I am training SqueezeNet on my own face database created from YouTube videos. Apparently the training accuracy is not improving to this magic number of ~0.37. In order to deal with label noise I have tried to include

Are there any known limitations or any help that can be provided would be useful.

Code Sample:

sqnet = SqueezeNet();
sqnet.layers[-1].activation = relu
sqnet.layers[-1].name = 'fc_1'
out = Dense(6, activation='softmax',
name='classifier')(sqnet.layers[-1].output)
sqnet = Model(sqnet.input, out)
for i in range(3,len(sqnet.layers)):
sqnet.layers[-i].trainable = False
sqnet.compile(Adam(),loss='categorical_crossentropy',
metrics=['acc'])
history = sqnet.fit_generator(
trg227,
steps_per_epoch=None,
epochs=2, verbose=1,
validation_data=valg227,
validation_steps=None,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0)