def cv_deep_learning_conv_out(X,
y,
nb_classes=3,
mode="CNNC_Name",
l=None,
param_d=None,
graph=True):
"""
l =[X.shape[1], 3, 30, nb_classes]
param_d = {"n_cv_flt": 3, "n_cv_ln": 50, "cv_activation": "relu"}
"""
KF = model_selection.KFold(5, shuffle=True)
kf = KF.split(X)
dcnn_score_l = []
c_wb_l = []
for tr, te in kf:
X_train, y_train = X[tr, :], y[tr]
X_test, y_test = X[te, :], y[te]
if mode == "CNNC_Name":
model = kkeras.CNNC_Name(param_d["n_cv_flt"],
param_d["n_cv_ln"],
param_d["cv_activation"],
l=l)
n_flt = param_d["n_cv_flt"]
elif mode == "CNNC_Name_Border":
model = kkeras.CNNC_Name_Border(param_d["n_cv_flt"],
param_d["n_cv_ln"],
param_d["cv_activation"],
l=l,
border_mode='valid')
n_flt = param_d["n_cv_flt"]
elif mode == "MLPC_Name":
model = kkeras.MLPC_Name(l=l)
n_flt = l[1]
else:
raise ValueError(
"The given mode is not supported: mode={}".format(mode))
model.fit(X_train,
y_train,
X_test,
y_test,
nb_classes,
batch_size=6,
nb_epoch=5)
dcnn_score = model.score(X_test, y_test)
dcnn_score_l.append(dcnn_score)
print("Accuracy:", dcnn_score)
c_w, c_b = model.get_c_wb()
print("c_w.shape=", c_w.shape)
c_w = c_w.reshape(-1, n_flt)
c_wb_l.append((c_w, c_b))
print(dcnn_score_l)
print("Mean:{0}, Std:{1}".format(np.mean(dcnn_score_l),
np.std(dcnn_score_l)))
if graph:
# One of weight vectors are drawn.
c_w = c_wb_l[0][0] # 0 = c_w, 1 = c_b
for ll in range(n_flt):
#plt.figure()
plt.plot(c_w[:, ll], label="Filter #{}".format(ll))
plt.legend()
return dcnn_score_l, c_wb_l
def cv_deep_learning_tsplot(X,
y,
nb_classes=3,
mode="CNNC_Name",
l=None,
param_d=None,
n_ensemble=1,
graph=True):
"""
l =[X.shape[1], 3, 30, nb_classes]
param_d = {"n_cv_flt": 3, "n_cv_ln": 50, "cv_activation": "relu"}
"""
ci = 95
dcnn_score_l = []
c_wb_l = []
c_w_l = []
for n_e in range(n_ensemble):
print("Index of Ensemble Iteration:", n_e)
KF = model_selection.KFold(5, shuffle=True)
kf = KF.split(X)
for tr, te in kf:
X_train, y_train = X[tr, :], y[tr]
X_test, y_test = X[te, :], y[te]
if mode == "CNNC_Name":
model = kkeras.CNNC_Name(param_d["n_cv_flt"],
param_d["n_cv_ln"],
param_d["cv_activation"],
l=l)
n_flt = param_d["n_cv_flt"]
elif mode == "CNNC_Name_Border":
model = kkeras.CNNC_Name_Border(param_d["n_cv_flt"],
param_d["n_cv_ln"],
param_d["cv_activation"],
l=l,
border_mode='valid')
n_flt = param_d["n_cv_flt"]
elif mode == "MLPC_Name":
model = kkeras.MLPC_Name(l=l)
n_flt = l[1]
else:
raise ValueError(
"The given mode is not supported: mode={}".format(mode))
model.fit(X_train,
y_train,
X_test,
y_test,
nb_classes,
batch_size=6,
nb_epoch=5)
dcnn_score = model.score(X_test, y_test)
dcnn_score_l.append(dcnn_score)
print("Accuracy:", dcnn_score)
c_w, c_b = model.get_c_wb()
print("c_w.shape=", c_w.shape)
c_w = c_w.reshape(-1, n_flt)
c_wb_l.append((c_w, c_b))
c_w_l.append(c_w)
print(dcnn_score_l)
print("Mean:{0}, Std:{1}".format(np.mean(dcnn_score_l),
np.std(dcnn_score_l)))
if graph:
c_w_a = np.array(c_w_l)
print(c_w_a.shape)
for i in range(c_w_a.shape[2]):
plt.subplot(1, c_w_a.shape[2], i + 1)
c_w_i = c_w_a[:, :, i]
sns.tsplot(c_w_i, ci=ci)
if i == 0:
plt.ylabel("Maganitue")
plt.xlabel('Time')
plt.title('Filter#{}'.format(i))
#plt.title( '{0} with ci={1}%'.format(mode, ci))
#if i < c_w_a.shape[2] - 1:
# plt.xticks([])
#else:
return dcnn_score_l