def get_predictions(data_dir, preprocessing_function=lambda x: x, model=model):
if isinstance(preprocessing_function, str):
if preprocessing_function == 'fliplr':
preprocessing_function = lambda x: x[..., ::-1, :]
elif preprocessing_function in ('identity', 'orig'):
preprocessing_function = lambda x: x
else:
raise ValueError('unknown preprocessing_function:\t%s' %
preprocessing_function)
val_datagen = ImageDataGenerator(**norm_params)
val_datagen.preprocessing_function = preprocessing_function
datagen_val_output = val_datagen.flow_from_directory(data_dir,
shuffle=False,
**flowfromdir_params)
gen_ = datagen_val_output
yhat = model.predict_generator(
gen_,
steps=len(gen_),
verbose=1,
)
dfdict = {"scores_%d" % nn: yy for nn, yy in enumerate(yhat.T)}
dfdict.update({"files": gen_.filenames, "label": gen_.classes})
dfres = pd.DataFrame(dfdict)
return dfres
seed=prms.seed)
norm_params = dict(
#rescale=prms.scaleup,
samplewise_center=prms.samplewise_center,
samplewise_std_normalization=prms.samplewise_center,
featurewise_center=False,
featurewise_std_normalization=False,
zca_whitening=False,
)
# In[23]:
train_datagen = ImageDataGenerator(**norm_params)
train_datagen.preprocessing_function = lambda x: x[..., ::-1, :] #*2**-8
datagen_train_output = train_datagen.flow_from_directory(
prms.data_train,
#stratify = prms.oversampling,
#sampling_factor=prms.sampling_factor,
#oversampling=prms.oversampling,
shuffle=False,
**flowfromdir_params)
SAMPLES_PER_EPOCH = len(datagen_train_output.filenames)
STEPS_PER_EPOCH = int(np.ceil(SAMPLES_PER_EPOCH / prms.batch_size))
##########################################
def get_predictions(data_dir, preprocessing_function=lambda x: x, model=model):
if isinstance(preprocessing_function, str):
if preprocessing_function == 'fliplr':