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':