diff --git a/.gitignore b/.gitignore
index 8cd80a983f6bcdd3f670d7c0e6d2c6bc58c276a0..328ca8326457062a6ce149f6fc5ee5695bb08e09 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@
MNIST_data/
*.png
*arrayparam*
+*debug*
# Byte-compiled / optimized / DLL files
__pycache__/
diff --git a/main/experiments/graph_drawing/till_october_2018/transfert_few_data/vgg_svhn_from_cifar100_deepstrom_few_data.py b/main/experiments/graph_drawing/till_october_2018/transfert_few_data/vgg_svhn_from_cifar100_deepstrom_few_data.py
index c40386f2524bc613efd1c0b96129685ba4fb7af7..978978b94b39a2c7bdeb2509350692fed240b60e 100644
--- a/main/experiments/graph_drawing/till_october_2018/transfert_few_data/vgg_svhn_from_cifar100_deepstrom_few_data.py
+++ b/main/experiments/graph_drawing/till_october_2018/transfert_few_data/vgg_svhn_from_cifar100_deepstrom_few_data.py
@@ -9,263 +9,246 @@ from skluc.main.utils import logger
matplotlib.rcParams.update({'font.size': 14})
-pd.set_option('display.width', 1000)
-
-DAT = ["SVHN"]
-DIR = ["/home/luc/Resultats/Deepstrom/SVHN/june_2018/svhn_few_data_debug_plus_dropout"]
-
-
-for h in range(len(DIR)):
- DATANAME = DAT[h]
- DIRNAME = DIR[h]
-
- FILENAME = "gathered_results.csv"
-
- min_acc = 0.00
- max_acc = 1.05
- # max_acc = 1.0
- linewidth = 0.9
- output_conv_dim = 512
- nb_classes = 10
-
- real_nys_marker = "s"
-
- learned_nys_marker = "x"
-
- linearity_color = "g"
-
- dense_marker = "v"
- dense_color = "r"
-
- deepfried_marker = "8"
- deepfried_color = "b"
-
- d_translate_kernel = {
- "linear": "Linear",
- "chi2_cpd": "Chi2",
- "rbf": "Gaussian"
- }
-
- if __name__ == '__main__':
- filepath = os.path.join(DIRNAME, FILENAME)
- field_names = ["method_name",
- "accuracy_val",
- "accuracy_test",
- "runtime",
- "number_epoch",
- "batch_size",
- "repr_dim",
- "two_layers_dense",
- "kernel_deepstrom",
- "gamma_kernel",
- "constante_sigmoid",
- "nb_layer_deepfried",
- "subsample_size",
- "validation_size",
- "seed",
- "non_linearity",
- "real_nystrom",
- "repr_quality",
- "train_size",
- "dropout"
- ]
-
- df = pd.read_csv(filepath, names=field_names)
- # df = df[df["accuracy_val"] != 'None']
- df = df.apply(pd.to_numeric, errors="ignore")
- method_names = set(df["method_name"].values)
- kernel_names = set(df["kernel_deepstrom"].values)
- kernel_names.remove("None")
- # kernel_names.remove("laplacian")
- repr_dim = set(df["repr_dim"].values)
- repr_dim.remove("None") # dtype: str
- # repr_dim.remove("16")
- nys_size = set(df["subsample_size"].values)
- nys_size.remove("None")
- nb_layers_deepfried = set(df["nb_layer_deepfried"].values)
- nb_layers_deepfried.remove("None")
- seed_values = set(df["seed"].values)
- batch_size = 128
- train_sizes = set(df["train_size"])
- dropout_values = set(df["dropout"].values)
- sigma_values = set(df["gamma_kernel"].values)
- sigma_values.remove("None")
- sigma_values = list(sigma_values)
- logger.debug("Nystrom possible sizes are: {}".format(nys_size))
- logger.debug("Kernel functions are: {}".format(kernel_names))
- logger.debug("Compared network types are: {}".format(method_names))
- logger.debug("Tested representation dimension are: {}".format(repr_dim))
-
- means_deepstrom = {}
-
- for t_size in sorted(list(train_sizes)):
- df_tsize = df[df["train_size"] == t_size]
- for drop_val in dropout_values:
- if int(drop_val) != 1:
- continue
- df_drop = df_tsize[df_tsize["dropout"] == drop_val]
-
- # plot deepstrom
- # ==============
- df_deepstrom = df_drop[df_drop["method_name"] == "deepstrom"]
- df_deepstrom["subsample_size"] = df_deepstrom["subsample_size"].astype(np.int)
- df_deepstrom_sort = df_deepstrom.sort_values(by=["subsample_size"])
- for i, k_name in enumerate(sorted(kernel_names)):
- if k_name != "rbf":
- df_deepstrom_kernels = [df_deepstrom_sort[df_deepstrom_sort["kernel_deepstrom"] == k_name]]
- else:
- df_deepstrom_kernels = []
- df_deepstrom_kernel_tmp = df_deepstrom_sort[df_deepstrom_sort["kernel_deepstrom"] == k_name]
- for sig_val in sigma_values:
- if float(sig_val) != 0.1:
- continue
- df_deepstrom_kernels.append(
- df_deepstrom_kernel_tmp[df_deepstrom_kernel_tmp["gamma_kernel"] == sig_val])
-
- for j, df_deepstrom_kernel in enumerate(df_deepstrom_kernels):
- f, ax = plt.subplots()
- # non_lin_dfs = {
- # "linear": df_deepstrom_kernel[df_deepstrom_kernel["non_linearity"] == "None"],
- # }
- # get the results of learned nystrom
- df_deepstrom_kernel_w = df_deepstrom_kernel[df_deepstrom_kernel["real_nystrom"] == False]
- np_deepstrom_kernel_w_mean_accuracy_test = np.mean(np.array([
- list(df_deepstrom_kernel_w[df_deepstrom_kernel_w["seed"] == seed_v]["accuracy_test"]) for
- seed_v in seed_values
- ]), axis=0)
- np_deepstrom_kernel_w_std_accuracy_test = np.std(np.array(
- [list(df_deepstrom_kernel_w[df_deepstrom_kernel_w["seed"] == seed_v]["accuracy_test"]) for
- seed_v in
- seed_values]), axis=0)
- np_param_nbr_deepstrom_kernel_w = (
- np.square(np.array(sorted(set(df_deepstrom_kernel_w["subsample_size"])))) + # m x m
- np.array(
- sorted(set(df_deepstrom_kernel_w["subsample_size"]))) * output_conv_dim + # m x d
- np.array(
- sorted(list(set(df_deepstrom_kernel_w["subsample_size"])))) * nb_classes) # m x c
-
- ax.errorbar(np_param_nbr_deepstrom_kernel_w,
- np_deepstrom_kernel_w_mean_accuracy_test,
- np_deepstrom_kernel_w_std_accuracy_test,
- marker=learned_nys_marker, color=linearity_color,
- label="Adaptative Deepström",
- capsize=3)
-
- # get the results of vanilla nystrom
- df_deepstrom_kernel_k = df_deepstrom_kernel[df_deepstrom_kernel["real_nystrom"]]
- if len(df_deepstrom_kernel_k):
- np_deepstrom_kernel_k_mean_accuracy_test = np.mean(
- np.array([list(
- df_deepstrom_kernel_k[df_deepstrom_kernel_k["seed"] == seed_v]["accuracy_test"]) for
- seed_v in
- seed_values]), axis=0)
- np_deepstrom_kernel_k_std_accuracy_test = np.std(
- np.array([list(
- df_deepstrom_kernel_k[df_deepstrom_kernel_k["seed"] == seed_v]["accuracy_test"]) for
- seed_v in
- seed_values]), axis=0)
-
- np_param_nbr_deepstrom_kernel_k = (
- np.square(np.array(sorted(set(df_deepstrom_kernel_k["subsample_size"])))) + # m x m
- np.array(sorted(
- set(df_deepstrom_kernel_k["subsample_size"]))) * output_conv_dim + # m x d
- np.array(sorted(
- list(set(df_deepstrom_kernel_k["subsample_size"])))) * nb_classes) # m x c
-
- ax.errorbar(np_param_nbr_deepstrom_kernel_k,
- np_deepstrom_kernel_k_mean_accuracy_test,
- np_deepstrom_kernel_k_std_accuracy_test,
- marker=real_nys_marker, color=linearity_color,
- label="Deepström",
- capsize=3)
-
- # plot dense
- # ==========
- df_dense = df_drop[df_drop["method_name"] == "dense"]
- df_dense = df_dense[df_dense["train_size"] == t_size]
- df_dense["repr_dim"] = df_dense["repr_dim"].astype(np.int)
- df_dense = df_dense.sort_values(by=["repr_dim"])
- np_dense_mean_accuracy_test = np.mean(
- np.array([list(df_dense[df_dense["seed"] == seed_v]["accuracy_test"]) for seed_v in
- seed_values]), axis=0)
- np_dense_std_accuracy_test = np.std(
- np.array([list(df_dense[df_dense["seed"] == seed_v]["accuracy_test"]) for seed_v in
- seed_values]), axis=0)
- ax.errorbar(
- np.array(sorted([int(n) for n in np.unique(df_dense["repr_dim"])])) * output_conv_dim +
- np.array(sorted([int(n) for n in np.unique(df_dense["repr_dim"])])) * nb_classes,
- np_dense_mean_accuracy_test,
- np_dense_std_accuracy_test,
- color=dense_color,
- marker=dense_marker,
- label="Fully Connected", capsize=3)
-
- # plot deepfried
- # ==============
- df_deepfried = df_drop[df_drop["method_name"] == "deepfriedconvnet"]
- np_deepfried_mean_accuracy_test = []
- np_deepfried_std_accuracy_test = []
- for l_nb in sorted(nb_layers_deepfried):
- df_deepfried_stack = df_deepfried[df_deepfried["nb_layer_deepfried"] == l_nb]
- np_deepfried_mean_accuracy_test.append(np.mean(df_deepfried_stack["accuracy_test"]))
- np_deepfried_std_accuracy_test.append(np.std(df_deepfried_stack["accuracy_test"]))
-
- ax.errorbar([(output_conv_dim * 3 + output_conv_dim * nb_classes) * i for i in [1]],
- np_deepfried_mean_accuracy_test,
- np_deepfried_std_accuracy_test,
- color=deepfried_color,
- marker=deepfried_marker,
-
- label="Adaptative DeepFriedConvnet", capsize=3)
- ax.set_ylim(min_acc, max_acc)
- ax.set_ylabel("Accuracy")
- ax.set_xticks([1e4, 1e5, 1e6])
- # if i == 2:
- # ax.set_xlabel("# Parameters")
- ax.set_xlabel("# Parameters")
- ax.legend(bbox_to_anchor=(0.5, -0.20), loc="upper center", ncol=2)
- ax.set_xticklabels([1e4, 1e5, 1e6])
- # else:
- # ax.set_xticklabels([])
- ax.set_xscale("symlog")
-
- ax_twin = ax.twiny()
- ax_twin.set_xscale("symlog")
- ax_twin.set_xlim(ax.get_xlim())
- ax_twin.set_xticks(np_param_nbr_deepstrom_kernel_w)
-
- # if i == 0:
- ax_twin.set_xlabel("Subsample Size")
- ax.set_title(
- "{} Kernel - {} - Train size: {}".format(d_translate_kernel[k_name], DATANAME, t_size),
- y=1.2)
- ax_twin.set_xticklabels(sorted(set(df_deepstrom_kernel_w["subsample_size"])))
- # else:
- # ax.set_title("Noyau {} - {} - Train size: {}".format(d_translate_kernel[k_name], DATANAME, t_size))
- # ax_twin.set_xticklabels([])
-
- f.set_size_inches(8, 6)
- f.tight_layout()
- f.subplots_adjust(bottom=0.3)
- # f.show()
- # exit()
- # learnable: change legend
- # ODIR = [
- # "/home/luc/PycharmProjects/deepFriedConvnets/main/experiments/graph_drawing/paper/svhn/few_data/parameters/dropout_{}".format(
- # str(drop_val).replace(".", "-"))]
- # out_dir_path = ODIR[h]
-
-
- if k_name != "rbf":
- out_name = "acc_param_tsize_{}_{}_{}".format(t_size, str(drop_val).replace(".", "-"),
- k_name)
- else:
- out_name = "acc_param_tsize_{}_{}_{}_{}".format(t_size, str(drop_val).replace(".", "-"),
- k_name,
- str(sigma_values[j]).replace(".", "-"))
-
- base_out_dir = os.path.join(os.path.abspath(__file__.split(".")[0]), "images")
- pathlib.Path(base_out_dir).mkdir(parents=True, exist_ok=True)
- out_path = os.path.join(base_out_dir, out_name)
- logger.debug(out_path)
- f.savefig(out_path)
+# pd.set_option('display.width', 1000)
+pd.set_option('display.expand_frame_repr', False)
+
+# DAT = ["SVHN"]
+# DIR = ["/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"]
+
+
+DATANAME = "SVHN"
+DIRNAME = "/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"
+
+FILENAME = "gathered_results_all.csv"
+
+min_acc = 0.00
+max_acc = 1.05
+# max_acc = 1.0
+linewidth = 0.9
+output_conv_dim = 512
+nb_classes = 10
+
+real_nys_marker = "s"
+
+learned_nys_marker = "x"
+
+linearity_color = "g"
+
+dense_marker = "v"
+dense_color = "r"
+
+deepfried_marker = "8"
+deepfried_color = "b"
+
+d_translate_kernel = {
+ "linear": "Linear",
+ "chi2_cpd": "Chi2",
+ "rbf": "Gaussian"
+}
+
+if __name__ == '__main__':
+ filepath = os.path.join(DIRNAME, FILENAME)
+ field_names = ["method_name",
+ "accuracy_val",
+ "accuracy_test",
+ "runtime_train",
+ "runtime_val",
+ "runtime_test",
+ "number_epoch",
+ "batch_size",
+ "repr_dim",
+ "second_layer_size",
+ "kernel_deepstrom",
+ "gamma_kernel",
+ "constante_sigmoid",
+ "nb_layer_deepfried",
+ "subsample_size",
+ "validation_size",
+ "seed",
+ "act",
+ "non_linearity",
+ "real_nystrom",
+ "repr_quality",
+ "train_size",
+ "dropout",
+ "dataset",
+ "real_deepfried"
+ ]
+
+ df = pd.read_csv(filepath, names=field_names)
+ df = df[df["accuracy_val"] != 'None']
+ df = df.apply(pd.to_numeric, errors="ignore")
+ df = df.drop_duplicates()
+ method_names = set(df["method_name"].values)
+ kernel_names = set(df["kernel_deepstrom"].values)
+ kernel_names.remove("None")
+ # kernel_names.remove("laplacian")
+ repr_dim = set(df["repr_dim"].values)
+ repr_dim.remove("None") # dtype: str
+ # repr_dim.remove("16")
+ nys_size = set(df["subsample_size"].values)
+ nys_size.remove("None")
+ nb_layers_deepfried = set(df["nb_layer_deepfried"].values)
+ nb_layers_deepfried.remove("None")
+ seed_values = set(df["seed"].values)
+ batch_size = 128
+ train_sizes = set(df["train_size"])
+
+ cut_layers = set(df["repr_quality"].values)
+
+ logger.debug("Nystrom possible sizes are: {}".format(nys_size))
+ logger.debug("Kernel functions are: {}".format(kernel_names))
+ logger.debug("Compared network types are: {}".format(method_names))
+ logger.debug("Tested representation dimension are: {}".format(repr_dim))
+
+ means_deepstrom = {}
+
+ for t_size in sorted(list(train_sizes)):
+ df_tsize = df[df["train_size"] == t_size]
+
+ for cut_layer in cut_layers:
+ df_cut_layer = df_tsize[df_tsize["repr_quality"] == cut_layer]
+
+ # plot deepstrom
+ # ==============
+ df_deepstrom = df_cut_layer[df_cut_layer["method_name"] == "deepstrom"]
+ df_deepstrom["subsample_size"] = df_deepstrom["subsample_size"].astype(np.int)
+ df_deepstrom_sort = df_deepstrom.sort_values(by=["subsample_size"])
+ for k_name in sorted(kernel_names):
+ df_deepstrom_kernel = df_deepstrom_sort[df_deepstrom_sort["kernel_deepstrom"] == k_name]
+
+ f, ax = plt.subplots()
+
+ # get the results of learned nystrom
+ df_deepstrom_kernel_w = df_deepstrom_kernel[df_deepstrom_kernel["real_nystrom"] == False]
+ all_accs_w = np.array([
+ list(df_deepstrom_kernel_w[df_deepstrom_kernel_w["seed"] == seed_v]["accuracy_test"]) for
+ seed_v in seed_values
+ ])
+ np_deepstrom_kernel_w_mean_accuracy_test = np.mean(all_accs_w, axis=0)
+ np_deepstrom_kernel_w_std_accuracy_test = np.std(all_accs_w, axis=0)
+ np_param_nbr_deepstrom_kernel_w = (
+ np.square(np.array(sorted(set(df_deepstrom_kernel_w["subsample_size"])))) + # m x m
+ np.array(
+ sorted(set(df_deepstrom_kernel_w["subsample_size"]))) * output_conv_dim + # m x d
+ np.array(
+ sorted(list(set(df_deepstrom_kernel_w["subsample_size"])))) * nb_classes) # m x c
+
+ ax.errorbar(np_param_nbr_deepstrom_kernel_w,
+ np_deepstrom_kernel_w_mean_accuracy_test,
+ np_deepstrom_kernel_w_std_accuracy_test,
+ marker=learned_nys_marker, color=linearity_color,
+ label="Adaptative Deepström",
+ capsize=3)
+
+ # get the results of vanilla nystrom
+ df_deepstrom_kernel_k = df_deepstrom_kernel[df_deepstrom_kernel["real_nystrom"]]
+ if len(df_deepstrom_kernel_k):
+ all_accs_k = np.array([
+ list(df_deepstrom_kernel_k[df_deepstrom_kernel_k["seed"] == seed_v]["accuracy_test"]) for
+ seed_v in seed_values
+ ])
+ np_deepstrom_kernel_k_mean_accuracy_test = np.mean(all_accs_k, axis=0)
+ np_deepstrom_kernel_k_std_accuracy_test = np.std(all_accs_k, axis=0)
+
+ np_param_nbr_deepstrom_kernel_k = (
+ np.square(np.array(sorted(set(df_deepstrom_kernel_k["subsample_size"])))) + # m x m
+ np.array(sorted(
+ set(df_deepstrom_kernel_k["subsample_size"]))) * output_conv_dim + # m x d
+ np.array(sorted(
+ list(set(df_deepstrom_kernel_k["subsample_size"])))) * nb_classes) # m x c
+
+ ax.errorbar(np_param_nbr_deepstrom_kernel_k,
+ np_deepstrom_kernel_k_mean_accuracy_test,
+ np_deepstrom_kernel_k_std_accuracy_test,
+ marker=real_nys_marker, color=linearity_color,
+ label="Deepström",
+ capsize=3)
+
+ # plot dense
+ # ==========
+ df_dense = df_cut_layer[df_cut_layer["method_name"] == "dense"]
+ df_dense = df_dense[df_dense["train_size"] == t_size]
+ df_dense["repr_dim"] = df_dense["repr_dim"].astype(np.int)
+ df_dense = df_dense.sort_values(by=["repr_dim"])
+ np_dense_mean_accuracy_test = np.mean(
+ np.array([list(df_dense[df_dense["seed"] == seed_v]["accuracy_test"]) for seed_v in
+ seed_values]), axis=0)
+ np_dense_std_accuracy_test = np.std(
+ np.array([list(df_dense[df_dense["seed"] == seed_v]["accuracy_test"]) for seed_v in
+ seed_values]), axis=0)
+ ax.errorbar(
+ np.array(sorted([int(n) for n in np.unique(df_dense["repr_dim"])])) * output_conv_dim +
+ np.array(sorted([int(n) for n in np.unique(df_dense["repr_dim"])])) * nb_classes,
+ np_dense_mean_accuracy_test,
+ np_dense_std_accuracy_test,
+ color=dense_color,
+ marker=dense_marker,
+ label="Fully Connected", capsize=3)
+
+ # # plot deepfried
+ # # ==============
+ df_deepfried = df_cut_layer[df_cut_layer["method_name"] == "deepfriedconvnet"]
+ np_deepfried_mean_accuracy_test = []
+ np_deepfried_std_accuracy_test = []
+ for l_nb in sorted(nb_layers_deepfried):
+ df_deepfried_stack = df_deepfried[df_deepfried["nb_layer_deepfried"] == l_nb]
+ if len(df_deepfried_stack):
+ np_deepfried_mean_accuracy_test.append(np.mean(df_deepfried_stack["accuracy_test"]))
+ np_deepfried_std_accuracy_test.append(np.std(df_deepfried_stack["accuracy_test"]))
+
+ nb_param_vals = [(output_conv_dim * 3 + output_conv_dim * nb_classes) * int(i) for i in sorted(set(df_deepfried["nb_layer_deepfried"].values))]
+ ax.errorbar(nb_param_vals,
+ np_deepfried_mean_accuracy_test,
+ np_deepfried_std_accuracy_test,
+ color=deepfried_color,
+ marker=deepfried_marker,
+ label="Adaptative DeepFriedConvnet", capsize=3)
+
+
+ ax.set_ylim(min_acc, max_acc)
+ ax.set_ylabel("Accuracy")
+ ax.set_xticks([1e4, 1e5, 1e6])
+ # if i == 2:
+ # ax.set_xlabel("# Parameters")
+ ax.set_xlabel("# Parameters")
+ ax.legend(bbox_to_anchor=(0.5, -0.20), loc="upper center", ncol=2)
+ ax.set_xticklabels([1e4, 1e5, 1e6])
+ # else:
+ # ax.set_xticklabels([])
+ ax.set_xscale("symlog")
+
+ ax_twin = ax.twiny()
+ ax_twin.set_xscale("symlog")
+ ax_twin.set_xlim(ax.get_xlim())
+ ax_twin.set_xticks(np_param_nbr_deepstrom_kernel_w)
+
+ # if i == 0:
+ ax_twin.set_xlabel("Subsample Size")
+ ax.set_title(
+ "{} Kernel - {} - Train size: {}".format(d_translate_kernel[k_name], DATANAME, t_size),
+ y=1.2)
+ ax_twin.set_xticklabels(sorted(set(df_deepstrom_kernel_w["subsample_size"])))
+ # else:
+ # ax.set_title("Noyau {} - {} - Train size: {}".format(d_translate_kernel[k_name], DATANAME, t_size))
+ # ax_twin.set_xticklabels([])
+
+ f.set_size_inches(8, 6)
+ f.tight_layout()
+ f.subplots_adjust(bottom=0.3)
+ # f.show()
+ # exit()
+ # learnable: change legend
+ # ODIR = [
+ # "/home/luc/PycharmProjects/deepFriedConvnets/main/experiments/graph_drawing/paper/svhn/few_data/parameters/dropout_{}".format(
+ # str(drop_val).replace(".", "-"))]
+ # out_dir_path = ODIR[h]
+
+
+ out_name = "acc_param_tsize_{}_{}_{}".format(t_size, cut_layer, k_name)
+
+ base_out_dir = os.path.join(os.path.abspath(__file__.split(".")[0]), "images")
+ pathlib.Path(base_out_dir).mkdir(parents=True, exist_ok=True)
+ out_path = os.path.join(base_out_dir, out_name)
+ logger.debug(out_path)
+ f.savefig(out_path)
diff --git a/main/experiments/graph_drawing/till_october_2018/transfert_few_data_cifar100_from_cifar10/vgg_deepstrom_few_data_cifar100_from_cifar10.py b/main/experiments/graph_drawing/till_october_2018/transfert_few_data_cifar100_from_cifar10/vgg_deepstrom_few_data_cifar100_from_cifar10.py
index 978978b94b39a2c7bdeb2509350692fed240b60e..3ca6a3592a699ad5824b15fc5536fa28f3f9da71 100644
--- a/main/experiments/graph_drawing/till_october_2018/transfert_few_data_cifar100_from_cifar10/vgg_deepstrom_few_data_cifar100_from_cifar10.py
+++ b/main/experiments/graph_drawing/till_october_2018/transfert_few_data_cifar100_from_cifar10/vgg_deepstrom_few_data_cifar100_from_cifar10.py
@@ -13,13 +13,13 @@ matplotlib.rcParams.update({'font.size': 14})
pd.set_option('display.expand_frame_repr', False)
# DAT = ["SVHN"]
-# DIR = ["/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"]
+# DIR = ["/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data_cifar100_from_cifar10"]
-DATANAME = "SVHN"
+DATANAME = "CIFAR100"
DIRNAME = "/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"
-FILENAME = "gathered_results_all.csv"
+FILENAME = "gathered_results.csv"
min_acc = 0.00
max_acc = 1.05
@@ -72,7 +72,8 @@ if __name__ == '__main__':
"train_size",
"dropout",
"dataset",
- "real_deepfried"
+ "real_deepfried",
+ "weights"
]
df = pd.read_csv(filepath, names=field_names)
diff --git a/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data.yml b/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data.yml
index 2224de04489bc0d546da6f22f2de037266eced93..4dc38842bd4650815b3e1df3502179acc7ec1b93 100644
--- a/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data.yml
+++ b/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data.yml
@@ -1,22 +1,18 @@
all:
dense:
deepfried:
- deepstrom_real_gamma:
- deepstrom_real_no_kernel_param:
- deepstrom_learned_gamma:
- deepstrom_learned_no_kernel_param:
+ deepstrom:
base:
- epoch_numbers: {"-e": [50]}
+ epoch_numbers: {"-e": [100]}
batch_sizes: {"-s": [64]}
val_size: {"-v": [10000]}
- seed: {"-a": "range(5)"}
- dropout: {"-d": [0.5, 0.7, 1.0]}
+ seed: {"-a": "range(10)"}
+ quiet: ["-q"]
data_size: {"-t":[20, 50, 100, 200, 500, 1000, 2000]}
dataset: ["--svhn"]
-
-gammavalue:
- gamma: {"-g": [0.0001, 0.001, 0.0025145440260884045, 0.01, 0.1]}
+ weights: {"-W": ["cifar100"]}
+ cut_layer: {"-B": ["block3_pool", "block5_conv4", "block5_pool"]}
dense:
network: ["dense"]
@@ -26,36 +22,12 @@ dense:
deepfried:
network: ["deepfriedconvnet"]
base:
- gammavalue:
- nbstacks: {"-N": [1]}
+ nbstacks: {"-N": [1, 3, 5, 7]}
-deepstrom_base:
+deepstrom:
network: ["deepstrom"]
base:
+ real_nys: ["-r", ""]
nys_size: {"-m": [16, 64, 128, 256, 512, 1024]}
-
-deepstrom_real:
- deepstrom_base:
- real_nys: ["-r"]
-
-deepstrom_real_gamma:
- deepstrom_real:
- gammavalue:
- kernel: ["-R"]
-
-deepstrom_real_no_kernel_param:
- deepstrom_real:
- kernel: ["-C", "-L"]
-
-deepstrom_learned:
- deepstrom_base:
-
-deepstrom_learned_gamma:
- deepstrom_learned:
- gammavalue:
- kernel: ["-R"]
-
-deepstrom_learned_no_kernel_param:
- deepstrom_learned:
- kernel: ["-C", "-L"]
+ kernel: ["-C", "-L", "-R"]
diff --git a/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data_cifar100_from_cifar10.yml b/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data_cifar100_from_cifar10.yml
index 4dc38842bd4650815b3e1df3502179acc7ec1b93..744e7443fb60e0c0d4aac1f2ca35cac9abfd2a5b 100644
--- a/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data_cifar100_from_cifar10.yml
+++ b/main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data_cifar100_from_cifar10.yml
@@ -10,8 +10,8 @@ base:
seed: {"-a": "range(10)"}
quiet: ["-q"]
data_size: {"-t":[20, 50, 100, 200, 500, 1000, 2000]}
- dataset: ["--svhn"]
- weights: {"-W": ["cifar100"]}
+ dataset: ["--cifar100"]
+ weights: {"-W": ["cifar10"]}
cut_layer: {"-B": ["block3_pool", "block5_conv4", "block5_pool"]}
dense: