def plot_factors(spread_factors,
save_name,
x_lim,
y_lim,
show=False,
train_size=1000,
font_size=18):
setting_spread = copy.deepcopy(setting)
setting_spread.update({
"network_name": "SpreadNet",
"line_title2": "$Spread_{PH}$",
"plot_colors": ["r", "g", "b"],
"plot_marker": [" "],
"train_size": train_size,
})
setting_dense_spread = copy.deepcopy(setting)
setting_dense_spread.update({
"network_name": "DenseSpreadNet",
"line_title2": "$MLP_{RT}$",
"plot_colors": ["r", "g", "b"],
"plot_marker": ["-"],
"train_size": train_size,
})
settings_spread = []
settings_dense_spread = []
colors = ["r", "g", "b", "y", "g", "b"]
for spread_factor, color in zip(spread_factors, colors):
print(spread_factor)
s_spread = copy.deepcopy(setting_spread)
s_dense_spread = copy.deepcopy(setting_dense_spread)
s_spread.update({
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": [" "],
"line_title2":
s_spread['line_title2'] + " sf " + str(spread_factor)
})
s_dense_spread.update({
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": ["h"],
"line_title2":
s_dense_spread['line_title2'] + " sf " + str(spread_factor)
})
settings_spread.append(s_spread)
settings_dense_spread.append(s_dense_spread)
network_settings = {
"SpreadNet": settings_spread,
# "DenseSpreadNet": settings_dense_spread
}
plot.create_plot(network_settings,
save_name,
x_lim,
y_lim,
font_size=font_size)
if show:
plt.show()
def plot_train_size(train_size, save_name, x_lim, y_lim, show=False):
setting_spread = copy.deepcopy(setting)
setting_spread.update({
"network_name": "SpreadNet",
"line_title2": "$Spread_{PH}$",
"plot_colors": ["g"],
"train_size": train_size
})
setting_dense = copy.deepcopy(setting)
setting_dense.update({
"network_name": "DenseNet",
"line_title2": "$MLP_{best}$",
"plot_colors": ["r"],
"spread_factor": 1,
"train_size": train_size
})
setting_dense_spread = copy.deepcopy(setting)
setting_dense_spread.update({
"network_name": "DenseSpreadNet",
"line_title2": "$MLP_{RT}$",
"plot_colors": ["b"],
"train_size": train_size
})
network_settings = {
"SpreadNet": [setting_spread],
"DenseNet": [setting_dense],
"DenseSpreadNet": [setting_dense_spread]
}
plot.create_plot(network_settings, save_name, x_lim, y_lim)
if show:
plt.plot()
def plot_batch(batch_sizes,
save_name,
x_lim,
y_lim,
show=False,
train_size=1000):
setting_spread = copy.deepcopy(setting)
setting_spread.update({
"network_name": "SpreadNet",
"line_title2": "$Spread_{PH}$",
"plot_colors": ["r", "g", "b"],
"plot_marker": [" "],
"train_size": train_size,
})
setting_dense_spread = copy.deepcopy(setting)
setting_dense_spread.update({
"network_name": "DenseSpreadNet",
"line_title2": "$MLP_{RT}$",
"plot_colors": ["r", "g", "b"],
"plot_marker": ["-"],
"train_size": train_size,
})
settings_spread = []
settings_dense_spread = []
colors = ["r", "g", "b", "y", "g", "b"]
for batch_size, color in zip(batch_sizes, colors):
print(batch_size)
s_spread = copy.deepcopy(setting_spread)
s_dense_spread = copy.deepcopy(setting_dense_spread)
s_spread.update({
"batch_size":
batch_size,
"plot_colors": [color],
"plot_markers": [" "],
"line_title2":
s_spread['line_title2'] + " batch size " + str(batch_size)
})
s_dense_spread.update({
"batch_size":
batch_size,
"plot_colors": [color],
"plot_markers": ["h"],
"line_title2":
s_dense_spread['line_title2'] + " batch size " + str(batch_size)
})
settings_spread.append(s_spread)
settings_dense_spread.append(s_dense_spread)
network_settings = {
"SpreadNet": settings_spread,
"DenseSpreadNet": settings_dense_spread
}
plot.create_plot(network_settings, save_name, x_lim, y_lim)
if show:
plt.show()
def plot_factors(spread_factors,
save_name,
x_lim,
y_lim,
show=False,
train_size=1000,
font_size=18):
setting_spread = copy.deepcopy(setting)
setting_spread.update({
"network_name": "SpreadNet",
"line_title2": "$Spread_{PH}$",
"plot_colors": ["r", "g", "b", "g"],
"plot_marker": [" "],
"train_size": train_size,
})
setting_spread_norm = copy.deepcopy(setting)
setting_spread_norm.update({
"network_name": "DenseNorm",
"line_title2": "$Spread_{V2}$",
"plot_colors": ["r", "g", "b", "g"],
"plot_marker": [" "],
"train_size": train_size,
})
setting_dense_batch = copy.deepcopy(setting)
setting_dense_batch.update({
"network_name": "DenseBatch",
"line_title2": "$DenseBatch_{RT}$",
"plot_colors": ["r", "g", "b", "g"],
"plot_marker": ["-"],
"train_size": train_size,
})
setting_mlp_best = copy.deepcopy(setting)
setting_mlp_best.update({
"network_name": "DenseNet",
"line_title2": "$MLP_{BEST}$",
"plot_colors": ["r", "g", "b", "g"],
"plot_marker": ["<"],
"train_size": train_size,
})
setting_spreadv3 = copy.deepcopy(setting)
setting_spreadv3.update({
"network_name": "SpreadV3",
"line_title2": "$Spread_{V3}$",
"plot_colors": ["r", "g", "b"],
"plot_marker": [" "],
"train_size": train_size
})
settings_spread_norm = []
settings_spread = []
settings_dense_batch = []
settings_spread_v3 = []
colors = ["r", "g", "b", "y", "g", "b", "g", "r"]
for spread_factor, color in zip(spread_factors, colors):
print(spread_factor)
s_spread_norm = copy.deepcopy(setting_spread_norm)
s_spread_norm.update({
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": [" "],
"line_title2":
s_spread_norm['line_title2'] + " sf " + str(spread_factor)
})
settings_spread_norm.append(s_spread_norm)
s_spread_v3 = copy.deepcopy(setting_spreadv3)
s_spread_v3.update({
"experiment":
"",
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": [">"],
"line_title2":
s_spread_v3['line_title2'] + " sf " + str(spread_factor)
})
settings_spread_v3.append(s_spread_v3)
s_spread = copy.deepcopy(setting_spread)
s_spread.update({
"experiment":
'3',
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": [">"],
"line_title2":
s_spread['line_title2'] + " sf " + str(spread_factor)
})
settings_spread.append(s_spread)
s_dense_spread = copy.deepcopy(setting_dense_batch)
s_dense_spread.update({
"spread_factor":
spread_factor,
"plot_colors": [color],
"plot_markers": ["h"],
"line_title2":
s_dense_spread['line_title2'] + " sf " + str(spread_factor)
})
settings_dense_batch.append(s_dense_spread)
settings_mlp_best = []
s_mlp_best = copy.deepcopy(setting_mlp_best)
s_mlp_best.update({
"experiment": '3',
"spread_factor": 1,
"plot_colors": ['silver'],
"plot_markers": ["<"],
"line_title2": s_mlp_best['line_title2']
})
settings_mlp_best.append(s_mlp_best)
network_settings = {
# "DenseNorm": settings_spread_norm,
# "DenseBatch": settings_dense_batch,
"SpreadV3": settings_spread_v3,
# "SpreadNet": settings_spread,
# "DenseNet": settings_mlp_best
}
plot.create_plot(network_settings,
save_name,
x_lim,
y_lim,
font_size=font_size,
show_acc=False,
show_loss=False)
if show:
plt.show()
def plot_train_size(train_size,
save_name,
x_lim,
y_lim,
show=False,
font_size=18):
setting_spread = copy.deepcopy(setting)
setting_spread.update({
"network_name": "SpreadNet",
"line_title2": "$Spread_{PH}$",
"plot_colors": ["g"],
"train_size": train_size
})
setting_dense = copy.deepcopy(setting)
setting_dense.update({
"network_name": "DenseNet",
"line_title2": "$MLP_{best}$",
"plot_colors": ["r"],
"spread_factor": 1,
"train_size": train_size
})
setting_dense_spread = copy.deepcopy(setting)
setting_dense_spread.update({
"network_name": "DenseSpreadNet",
"line_title2": "$MLP_{RT}$",
"plot_colors": ["b"],
"train_size": train_size
})
setting_spread_v3 = copy.deepcopy(setting)
setting_spread_v3.update({
"network_name": "SpreadV3",
"line_title2": "$Spread_{V3}$",
"plot_colors": ["y"],
"experiment": '',
"train_size": train_size
})
setting_dense_bn = copy.deepcopy(setting)
setting_dense_bn.update({
"network_name": "DenseNorm",
"line_title2": "$Dense_{BN}$",
"plot_colors": ["brown"],
"experiment": '',
"train_size": train_size
})
network_settings = {
"SpreadNet": [setting_spread],
"DenseNet": [setting_dense],
"DenseSpreadNet": [setting_dense_spread],
"SpreadV3": [setting_spread_v3],
"DenseNorm": [setting_dense_bn],
}
plot.create_plot(network_settings,
save_name,
x_lim,
y_lim,
font_size=font_size)
if show:
plt.plot()