train_neg_datapath = '/data/Kaggle/neg-filt-png'
# parameters
im_dims = (1024, 1024)
n_channels = 1
batch_size = 4
learnRate = 1e-4
filt_nums = 16
num_blocks = 5
val_split = .15
train_weights_filepath = 'Best_Kaggle_Classification_Weights_{}_v4.h5'
cur_weights_path = train_weights_filepath.format('1024train')
# datagen params
full_train_params = get_train_params(batch_size, (1024, 1024), 1)
full_val_params = get_val_params(batch_size, (1024, 1024), 1)
# Create model
full_model = Inception_model(input_shape=(1024, 1024)+(n_channels,))
full_model.load_weights(cur_weights_path)
# Get datagen
_, full_val_gen, _ = get_class_datagen(
train_pos_datapath, train_neg_datapath, full_train_params, full_val_params, val_split)
# Calculate confusion matrix
print('Calculating classification confusion matrix...')
full_val_gen.shuffle = False
preds = full_model.predict_generator(full_val_gen, verbose=1)
labels = [full_val_gen.labels[f] for f in full_val_gen.list_IDs]
y_pred = np.rint(preds)
batch_size = 4
batch_size_1024 = 1
learnRate = 1e-5
val_split = .15
epochs_unfreeze = [5, 10] # epochs before and after unfreezing weights
full_epochs = 30 # epochs trained on 1024 data with only large masks
#full_epochs_all = 10 # epochs trained on all positive masks
# model parameters
filt_nums = 16
num_blocks = 4
# datagen params
pre_train_params = get_train_params(pre_batch_size, pre_im_dims,
pre_n_channels)
pre_val_params = get_val_params(pre_batch_size, pre_im_dims, pre_n_channels)
train_params = get_train_params(batch_size, im_dims, n_channels)
val_params = get_val_params(batch_size, im_dims, n_channels)
full_train_params = get_train_params(batch_size_1024, (1024, 1024), 1)
full_val_params = get_val_params(batch_size_1024, (1024, 1024), 1)
#I've saved the preprocessed data with clahe, so remove it
#pre_train_params["preprocessing_function"] = 'None'
#pre_val_params["preprocessing_function"] = 'None'
train_params["preprocessing_function"] = 'None'
val_params["preprocessing_function"] = 'None'
full_train_params["preprocessing_function"] = 'None'
full_val_params["preprocessing_function"] = 'None'
# %% ~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~Pre-training~~~~~~