<jupyter_start><jupyter_text>1. Setup<jupyter_code>import sys
sys.path.append('../../..')
import matplotlib.pyplot as plt
import warnings

from experiments.experiment_utils import *
%matplotlib inline
%load_ext autoreload
%autoreload 2

warnings.filterwarnings('ignore')<jupyter_output><empty_output><jupyter_text>2. Experiment stats- Architecture: FCRN-A;- Train size: (32 vs 64);- Batch size: 16;- 5 runs with random train images;  2.1 Experiment 1- Loss: MSE;- with data augmentation (augment16);- without preprocessing steps;- full images;<jupyter_code>res_list = load_experiments_results('.', ['n_32', 'loss_mse', 'full_img'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_32', 'loss_mse', 'full_img']))
res_df.head()
res_list = load_experiments_results('.', ['n_64', 'loss_mse', 'full_img'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_64', 'loss_mse', 'full_img']))
res_df.head()<jupyter_output>n_64_sigma_5_randseed_641_loss_mse_full_img
n_64_sigma_5_randseed_642_loss_mse_full_img
n_64_sigma_5_randseed_644_loss_mse_full_img
n_64_sigma_5_randseed_645_loss_mse_full_img
n_64_sigma_5_randseed_643_loss_mse_full_img<jupyter_text>2.2 Experiment 2- Loss: LogCosh;- with data augmentation (augment16);- without preprocessing steps;- full images;<jupyter_code>res_list = load_experiments_results('.', ['n_32', 'loss_logcosh', 'full_img'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_32', 'loss_logcosh', 'full_img']))
res_df.head()
res_list = load_experiments_results('.', ['n_64', 'loss_logcosh', 'full_img'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_64', 'loss_logcosh', 'full_img']))
res_df.head()<jupyter_output>n_64_sigma_5_randseed_641_loss_logcosh_full_img
n_64_sigma_5_randseed_642_loss_logcosh_full_img
n_64_sigma_5_randseed_643_loss_logcosh_full_img
n_64_sigma_5_randseed_644_loss_logcosh_full_img
n_64_sigma_5_randseed_645_loss_logcosh_full_img<jupyter_text>2.3 Experiment 3- Loss: MSE;- with data augmentation (augment16);- without preprocessing steps;- patches: 4*(128x128);<jupyter_code>res_list = load_experiments_results('.', ['n_32', 'loss_mse', 'patch_4_128x128'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_32', 'loss_mse', 'patch_4_128x128']))
res_df.head()
res_list = load_experiments_results('.', ['n_64', 'loss_mse', 'patch_4_128x128'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_64', 'loss_mse', 'patch_4_128x128']))
res_df.head()<jupyter_output>n_64_sigma_5_randseed_641_loss_mse_patch_4_128x128
n_64_sigma_5_randseed_642_loss_mse_patch_4_128x128
n_64_sigma_5_randseed_643_loss_mse_patch_4_128x128
n_64_sigma_5_randseed_644_loss_mse_patch_4_128x128
n_64_sigma_5_randseed_645_loss_mse_patch_4_128x128<jupyter_text>2.4 Experiment 4- Loss: LogCosh;- with data augmentation (augment16);- without preprocessing steps;- patches: 4*(128x128);<jupyter_code>res_list = load_experiments_results('.', ['n_32', 'loss_logcosh', 'patch_4_128x128'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_32', 'loss_logcosh', 'patch_4_128x128']))
res_df.head()
res_list = load_experiments_results('.', ['n_64', 'loss_logcosh', 'patch_4_128x128'])
res_df = get_mean_and_std_of_experiments(res_list, ','.join(['vgg_cells', 'n_64', 'loss_logcosh', 'patch_4_128x128']))
res_df.head()<jupyter_output>n_64_sigma_5_randseed_641_loss_logcosh_patch_4_128x128
n_64_sigma_5_randseed_642_loss_logcosh_patch_4_128x128
n_64_sigma_5_randseed_643_loss_logcosh_patch_4_128x128
n_64_sigma_5_randseed_644_loss_logcosh_patch_4_128x128
n_64_sigma_5_randseed_645_loss_logcosh_patch_4_128x128
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