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Commit e8d3baa5 authored by Luc Giffon's avatar Luc Giffon
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update gitignore to not take files with debug in the name

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.gitignore
+ 1
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View file @ e8d3baa5
......@@ -3,6 +3,7 @@
MNIST_data/
*.png
*arrayparam*
*debug*
# Byte-compiled / optimized / DLL files
__pycache__/
......
main/experiments/graph_drawing/till_october_2018/transfert_few_data/vgg_svhn_from_cifar100_deepstrom_few_data.py
+ 243
− 260
View file @ e8d3baa5
......@@ -9,17 +9,17 @@ from skluc.main.utils import logger
matplotlib.rcParams.update({'font.size': 14})
pd.set_option('display.width', 1000)
# pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)
DAT = ["SVHN"]
DIR = ["/home/luc/Resultats/Deepstrom/SVHN/june_2018/svhn_few_data_debug_plus_dropout"]
# DAT = ["SVHN"]
# DIR = ["/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"]
for h in range(len(DIR)):
DATANAME = DAT[h]
DIRNAME = DIR[h]
DATANAME = "SVHN"
DIRNAME = "/home/luc/Resultats/Deepstrom/october_2018/transfert_few_data"
FILENAME = "gathered_results.csv"
FILENAME = "gathered_results_all.csv"
min_acc = 0.00
max_acc = 1.05
......@@ -51,11 +51,13 @@ for h in range(len(DIR)):
field_names = ["method_name",
"accuracy_val",
"accuracy_test",
"runtime",
"runtime_train",
"runtime_val",
"runtime_test",
"number_epoch",
"batch_size",
"repr_dim",
"two_layers_dense",
"second_layer_size",
"kernel_deepstrom",
"gamma_kernel",
"constante_sigmoid",
......@@ -63,16 +65,20 @@ for h in range(len(DIR)):
"subsample_size",
"validation_size",
"seed",
"act",
"non_linearity",
"real_nystrom",
"repr_quality",
"train_size",
"dropout"
"dropout",
"dataset",
"real_deepfried"
]
df = pd.read_csv(filepath, names=field_names)
# df = df[df["accuracy_val"] != 'None']
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")
......@@ -87,10 +93,9 @@ for h in range(len(DIR)):
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)
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))
......@@ -100,43 +105,28 @@ for h in range(len(DIR)):
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]
for cut_layer in cut_layers:
df_cut_layer = df_tsize[df_tsize["repr_quality"] == cut_layer]
# plot deepstrom
# ==============
df_deepstrom = df_drop[df_drop["method_name"] == "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 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):
for k_name in sorted(kernel_names):
df_deepstrom_kernel = df_deepstrom_sort[df_deepstrom_sort["kernel_deepstrom"] == k_name]
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([
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
]), 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_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(
......@@ -154,16 +144,12 @@ for h in range(len(DIR)):
# 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)
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
......@@ -181,7 +167,7 @@ for h in range(len(DIR)):
# plot dense
# ==========
df_dense = df_drop[df_drop["method_name"] == "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"])
......@@ -200,23 +186,26 @@ for h in range(len(DIR)):
marker=dense_marker,
label="Fully Connected", capsize=3)
# plot deepfried
# ==============
df_deepfried = df_drop[df_drop["method_name"] == "deepfriedconvnet"]
# # 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"]))
ax.errorbar([(output_conv_dim * 3 + output_conv_dim * nb_classes) * i for i in [1]],
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])
......@@ -256,13 +245,7 @@ for h in range(len(DIR)):
# 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(".", "-"))
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)
......
main/experiments/graph_drawing/till_october_2018/transfert_few_data_cifar100_from_cifar10/vgg_deepstrom_few_data_cifar100_from_cifar10.py
+ 5
− 4
View file @ e8d3baa5
......@@ -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)
......
main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data.yml
+ 10
− 38
View file @ e8d3baa5
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"]
main/experiments/parameter_files/october_2018/lazyfile_transfert_few_data_cifar100_from_cifar10.yml
+ 2
− 2
View file @ e8d3baa5
......@@ -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:
......
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