def solve(self,
input_train_data,
target_train_data,
input_val_data,
target_val_data,
model,
callback=None):
if not isinstance(model, PerceptronModel):
raise ValueError(
'PerceptronSolver can only solve for PerceptronModel')
# convert numpy arrays to util.Counters and lists
print("Converting numpy arrays to counters and lists...")
rows = input_train_data.shape[0]
input_train_data = np.c_[input_train_data, np.ones((rows, 1))]
input_train_data = util.counters_from_numpy_array(input_train_data)
target_train_data = util.list_from_numpy_array_one_hot(
target_train_data)
rows = input_val_data.shape[0]
input_val_data = np.c_[input_val_data, np.ones((rows, 1))]
input_val_data = util.counters_from_numpy_array(input_val_data)
target_val_data = util.list_from_numpy_array_one_hot(target_val_data)
print("... done")
if callback is None or self.plot == 0:
train_callback = None
else:
train_callback = lambda: callback(model)
model.train(input_train_data,
target_train_data,
input_val_data,
target_val_data,
iterations=self.iterations,
callback=train_callback)
def solve(self, input_train_data, target_train_data, input_val_data, target_val_data, model, callback=None):
if not isinstance(model, PerceptronModel):
raise ValueError('PerceptronSolver can only solve for PerceptronModel')
# convert numpy arrays to util.Counters and lists
print("Converting numpy arrays to counters and lists...")
rows = input_train_data.shape[0]
input_train_data = np.c_[input_train_data, np.ones((rows,1))]
input_train_data = util.counters_from_numpy_array(input_train_data)
target_train_data = util.list_from_numpy_array_one_hot(target_train_data)
rows = input_val_data.shape[0]
input_val_data = np.c_[input_val_data, np.ones((rows,1))]
input_val_data = util.counters_from_numpy_array(input_val_data)
target_val_data = util.list_from_numpy_array_one_hot(target_val_data)
print("... done")
if callback is None or self.plot == 0:
train_callback = None
else:
train_callback = lambda: callback(model)
model.train(input_train_data, target_train_data,
input_val_data, target_val_data,
iterations=self.iterations, callback=train_callback)
def accuracy(self, input_data, target_data):
if isinstance(input_data, np.ndarray):
input_data = util.counters_from_numpy_array(input_data)
if isinstance(target_data, np.ndarray):
target_data = util.list_from_numpy_array_one_hot(target_data)
return PerceptronClassifier.accuracy(self, input_data, target_data)
def accuracy(self, input_data, target_data):
if isinstance(input_data, np.ndarray):
input_data = util.counters_from_numpy_array(input_data)
if isinstance(target_data, np.ndarray):
target_data = util.list_from_numpy_array_one_hot(target_data)
return PerceptronClassifier.accuracy(self, input_data, target_data)
