def accuracy(self, x, t, func):
self.func['evaluation'] = _CallFunction('common.functions', func)
y = self.predict(x)
acc = self.func['evaluation'](y, t)
return acc
def compile(self, loss, optimizer='sgd', metrics=['accuracy']):
self.func['loss'] = _CallClass('common.layers', loss)
self.func['loss'] = self.func['loss']()
self.func['optimizer'] = _CallClass('common.optimizer', optimizer)
self.func['optimizer'] = self.func['optimizer']()
#----------------------------------------
# 精度検証用の関数を設定
#----------------------------------------
param = ['train_', 'val_']
for metric in metrics:
if str(metric).lower() in ('r2'):
metric = 'r2_score'
if str(metric).lower() in ('rsme'):
metric = 'rsme_score'
self.metrics_func[metric] = _CallFunction('common.functions',
metric)
#--------------------------
# 結果保存用の配列を設定
#--------------------------
for i in param:
name = str(i) + str(metric)
self.logs[name] = []
"""
def compile(self, loss, optimizer='sgd', lr=0.01, metrics=['r2', 'rmse']):
self.func['loss'] = _CallClass('common.layers', loss)
self.func['loss'] = self.func['loss']()
#----------------------------------------
#レイヤ内の設定
#----------------------------------------
y = np.zeros((1, self.units[1]))
for layer in self.sequential.values():
y = layer.compile(y, optimizer, lr)
#----------------------------------------
# 精度検証用の関数を設定
#----------------------------------------
param = ['train_', 'val_']
for metric in metrics:
if str(metric).lower() in ('r2'):
metric = 'r2_score'
if str(metric).lower() in ('rmse'):
metric = 'rmse_score'
#self.metrics_func[metric] = _CallFunction('common.functions', metric)
self.func[metric] = _CallFunction('common.functions', metric)
#--------------------------
# 結果保存用の配列を設定
#--------------------------
for i in param:
name = str(i) + str(metric)
self.logs[name] = []
#-----------------------------
# テスト結果保存用の配列を設定
#-----------------------------
self.score[metric] = []
def __InitBias__(self, rear_node, bias_initializer='zeros'):
"""
閾値の初期設定
Parameters
----------
bias_initializer : biasを指定
rear_node : 次のnodeの行数
"""
method = _CallFunction('common.bias', bias_initializer)
self.params['b'] = method(rear_node)
def __InitBias__(self, row, bias_initializer='zeros'):
"""
閾値の初期設定
Parameters
----------
row : 重みの行数
bias_initializer : biasを指定
"""
method = _CallFunction('common.bias', bias_initializer)
return method(row)
def compile(self, loss, optimizer='sgd', metrics=['accuracy']):
self.func['loss'] = _CallClass('common.layers', loss)
self.func['loss'] = self.func['loss']()
self.func['optimizer'] = _CallClass('common.optimizer', optimizer)
self.func['optimizer'] = self.func['optimizer']()
func = []
for metric in metrics:
if str(metric).lower() in ('r2'):
metric = 'r2_score'
if str(metric).lower() in ('rsme'):
metric = 'rsme_score'
func.append(_CallFunction('common.functions', metric))
for key in self.metrics_func.keys():
self.metrics_func[key] = func
def __InitWeight__(self, rear_node, weight_initializer='he'):
"""
重みの初期設定
Parameters
----------
weight_initializer : 重みの標準偏差を指定
'relu'または'he'を指定した場合は「Heの初期値」を設定
'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定
rear_node : 次のnodeの行数
"""
if str(weight_initializer).lower() in ('relu', 'he'):
weight_initializer = 'he_nomal'
elif str(weight_initializer).lower() in ('sigmoid', 'xavier'):
weight_initializer = 'glorot_uniform'
method = _CallFunction('common.weight', weight_initializer)
scale = method(self.units)
self.params['W'] = scale * np.random.randn(self.units, rear_node)
def __InitWeight__(self, row, col, n, weight_initializer='he'):
"""
重みの初期設定
Parameters
----------
row : 重みの行数
col : 重みの列数
n : 前の層のユニット数
weight_initializer : 重みの標準偏差を指定
'relu'または'he'を指定した場合は「Heの初期値」を設定
'sigmoid'または'xavier'を指定した場合は「Xavierの初期値」を設定
"""
if str(weight_initializer).lower() in ('relu', 'he'):
weight_initializer = 'he_nomal'
elif str(weight_initializer).lower() in ('sigmoid', 'xavier'):
weight_initializer = 'glorot_uniform'
method = _CallFunction('common.weight', weight_initializer)
scale = method(n)
return scale * np.random.randn(row, col)
def compile(self, loss, optimizer='sgd', lr=0.01, metrics=['r2', 'rmse']):
self.func['loss'] = _CallClass('common.layers', loss)
self.func['loss'] = self.func['loss']()
self.func['optimizer'] = _CallClass('common.optimizer', optimizer)
self.func['optimizer'] = self.func['optimizer']()
#----------------------------------------
#レイヤ内の設定
#----------------------------------------
idx = 1
y = np.zeros((1, self.inputLayer[1]))
for key, layer in self.sequential.items():
#y = layer.compile(y, optimizer, lr)
#key, y, params = layer.compile(y)
y = layer[0].compile(y)
if 'Dense' in key:
#self.Dense[key + str(idx)] = layer
self.params['W' + str(idx)] = layer[0].params['W']
self.params['b' + str(idx)] = layer[0].params['b']
idx += 1
#----------------------------------------
# 精度検証用の関数を設定
#----------------------------------------
param = ['train_', 'val_']
for metric in metrics:
if str(metric).lower() in ('r2'):
metric = 'r2_score'
if str(metric).lower() in ('rmse'):
metric = 'rmse_score'
#self.metrics_func[metric] = _CallFunction('common.functions', metric)
self.func[metric] = _CallFunction('common.functions', metric)
#--------------------------
# 結果保存用の配列を設定
#--------------------------
for i in param:
name = str(i) + str(metric)
self.logs[name] = []
#-----------------------------
# テスト結果保存用の配列を設定
#-----------------------------
self.score[metric] = []
def compile(self, loss, optimizer="sgd", lr=0.01, metrics=["r2", "rmse"]):
self.func["loss"] = _CallClass("common.layers", loss)
self.func["loss"] = self.func["loss"]()
self.func["optimizer"] = _CallClass("common.optimizer", optimizer)
self.func["optimizer"] = self.func["optimizer"]()
# ----------------------------------------
# レイヤ内の設定
# ----------------------------------------
idx = 1
y = np.zeros((1, self.inputLayer[1]))
for key, layer in self.sequential.items():
y = layer[0].compile(y)
if "Dense" in key:
# self.Dense[key + str(idx)] = layer
self.params["W" + str(idx)] = layer[0].params["W"]
self.params["b" + str(idx)] = layer[0].params["b"]
idx += 1
# ----------------------------------------
# 精度検証用の関数を設定
# ----------------------------------------
param = ["train_", "val_"]
for metric in metrics:
if str(metric).lower() in ("r2"):
metric = "r2_score"
if str(metric).lower() in ("rmse"):
metric = "rmse_score"
self.func[metric] = _CallFunction("common.functions", metric)
# --------------------------
# 結果保存用の配列を設定
# --------------------------
for i in param:
name = str(i) + str(metric)
self.logs[name] = []
# -----------------------------
# テスト結果保存用の配列を設定
# -----------------------------
self.score[metric] = []
def __init__(self):
self.loss = None #損失
self.y = None #softmaxの出力
self.t = None #教師データ
self.func = _CallFunction('common.functions', 'softmax')
self.loss = _CallFunction('common.functions', 'cross_entropy_error')
def __init__(self):
self.y = None # linerの出力
self.t = None # 教師データ
self.func = _CallFunction('common.functions', 'mean_squared_error')
