def splitset(self,
train_word_num,
train_label,
train_size=0.9,
random_state=1):
self.train_X, self.test_X, train_y, test_y = train_test_split(
train_word_num, train_label, train_size=0.9, random_state=1)
self.Y_train = np_utils.to_categorical(train_y, self.nb_classes)
self.Y_test = np_utils.to_categorical(test_y, self.nb_classes)
def score(self, x, y, **kwargs):
"""Returns the mean accuracy on the given test data and labels.
Arguments:
x: array-like, shape `(n_samples, n_features)`
Test samples where n_samples in the number of samples
and n_features is the number of features.
y: array-like, shape `(n_samples,)` or `(n_samples, n_outputs)`
True labels for x.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.evaluate`.
Returns:
score: float
Mean accuracy of predictions on X wrt. y.
Raises:
ValueError: If the underlying model isn't configured to
compute accuracy. You should pass `metrics=["accuracy"]` to
the `.compile()` method of the model.
"""
y = np.searchsorted(self.classes_, y)
kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)
loss_name = self.model.loss
if hasattr(loss_name, '__name__'):
loss_name = loss_name.__name__
if loss_name == 'categorical_crossentropy' and len(y.shape) != 2:
y = to_categorical(y)
outputs = self.model.evaluate(x, y, **kwargs)
if not isinstance(outputs, list):
outputs = [outputs]
for name, output in zip(self.model.metrics_names, outputs):
if name == 'acc':
return output
raise ValueError('The model is not configured to compute accuracy. '
'You should pass `metrics=["accuracy"]` to '
'the `model.compile()` method.')
def score(self, x, y, **kwargs):
"""Returns the mean accuracy on the given test data and labels.
Arguments:
x: array-like, shape `(n_samples, n_features)`
Test samples where `n_samples` is the number of samples
and `n_features` is the number of features.
y: array-like, shape `(n_samples,)` or `(n_samples, n_outputs)`
True labels for `x`.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.evaluate`.
Returns:
score: float
Mean accuracy of predictions on `x` wrt. `y`.
Raises:
ValueError: If the underlying model isn't configured to
compute accuracy. You should pass `metrics=["accuracy"]` to
the `.compile()` method of the model.
"""
y = np.searchsorted(self.classes_, y)
kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)
loss_name = self.model.loss
if hasattr(loss_name, '__name__'):
loss_name = loss_name.__name__
if loss_name == 'categorical_crossentropy' and len(y.shape) != 2:
y = to_categorical(y)
outputs = self.model.evaluate(x, y, **kwargs)
if not isinstance(outputs, list):
outputs = [outputs]
for name, output in zip(self.model.metrics_names, outputs):
if name == 'acc':
return output
raise ValueError('The model is not configured to compute accuracy. '
'You should pass `metrics=["accuracy"]` to '
'the `model.compile()` method.')
def generator(data_x, data_y, batch_size=32):
n_batches = math.ceil(len(data_x) / batch_size)
while True:
for i in range(n_batches):
start = i * batch_size
end = (i + 1) * batch_size
data_x_mb = data_x[start:end]
data_y_mb = data_y[start:end]
data_x_mb = np.array(data_x_mb).astype('float32') / 255.
data_y_mb = pad_sequences(data_y_mb,
dtype='int32',
padding='post',
value=w2i['<pad>'])
data_y_mb_oh = np.array([
np_utils.to_categorical(datum_y, vocab_size)
for datum_y in data_y_mb[:, 1:]
])
yield [data_x_mb, data_y_mb], data_y_mb_oh
def fit(self, x, y, **kwargs):
"""Constructs a new model with `build_fn` & fit the model to `(x, y)`.
Arguments:
x : array-like, shape `(n_samples, n_features)`
Training samples where n_samples in the number of samples
and n_features is the number of features.
y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)`
True labels for X.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.fit`
Returns:
history : object
details about the training history at each epoch.
"""
if self.build_fn is None:
self.model = self.__call__(**self.filter_sk_params(self.__call__))
elif (not isinstance(self.build_fn, types.FunctionType)
and not isinstance(self.build_fn, types.MethodType)):
self.model = self.build_fn(
**self.filter_sk_params(self.build_fn.__call__))
else:
self.model = self.build_fn(**self.filter_sk_params(self.build_fn))
loss_name = self.model.loss
if hasattr(loss_name, '__name__'):
loss_name = loss_name.__name__
if loss_name == 'categorical_crossentropy' and len(y.shape) != 2:
y = to_categorical(y)
fit_args = copy.deepcopy(self.filter_sk_params(Sequential.fit))
fit_args.update(kwargs)
history = self.model.fit(x, y, **fit_args)
return history
def fit(self, x, y, **kwargs):
"""Constructs a new model with `build_fn` & fit the model to `(x, y)`.
Arguments:
x : array-like, shape `(n_samples, n_features)`
Training samples where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like, shape `(n_samples,)` or `(n_samples, n_outputs)`
True labels for `x`.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.fit`
Returns:
history : object
details about the training history at each epoch.
"""
if self.build_fn is None:
self.model = self.__call__(**self.filter_sk_params(self.__call__))
elif (not isinstance(self.build_fn, types.FunctionType) and
not isinstance(self.build_fn, types.MethodType)):
self.model = self.build_fn(
**self.filter_sk_params(self.build_fn.__call__))
else:
self.model = self.build_fn(**self.filter_sk_params(self.build_fn))
loss_name = self.model.loss
if hasattr(loss_name, '__name__'):
loss_name = loss_name.__name__
if loss_name == 'categorical_crossentropy' and len(y.shape) != 2:
y = to_categorical(y)
fit_args = copy.deepcopy(self.filter_sk_params(Sequential.fit))
fit_args.update(kwargs)
history = self.model.fit(x, y, **fit_args)
return history
# mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size = 128
n_classes = 10
n_epoch = 12 # 训练轮数
img_rows, img_cols = 28, 28
n_filters = 32
poll_size = (2, 2) # 池化大小
kernel_size = (3, 3) # 卷积核大小
(train_x, train_y), (test_x, test_y) = mnist.load_data()
train_x = train_x.reshape(train_x.shape[0], img_rows, img_cols, 1)
test_x = test_x.reshape(test_x.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
train_x = train_x.astype('float32')
test_x = test_x.astype('float32')
train_x /= 255
test_x /= 255
# 将类向量转换成二进制矩阵
train_y = np_utils.to_categorical(train_y, n_classes)
test_y = np_utils.to_categorical(test_y, n_classes)
# 构建训练模型
model = Sequential()
# 添加卷积层
model.add(Convolution2D(n_filters, kernel_size[0],
kernel_size[1],
border_mode='valid',
input_shape=input_shape))
a0 = 0.440240602542388
a1 = -0.334501803331783
b1 = -0.198990393984879
a2 = -0.050159136439220
b2 = 0.099347477830878
w = 2 * np.pi
HEART_RATES = np.linspace(55, 240, 75)
NB_CLASSES = len(HEART_RATES)
# prepare labels and label categories
labels = np.zeros(NB_CLASSES + 1)
for i in range(NB_CLASSES + 1):
labels[i] = i
labels_cat = np_utils.to_categorical(labels)
EPOCHS = 5000
CONTINUE_TRAINING = False
SAVE_ALL_MODELS = False
train_loss = []
val_loss = []
train_acc = []
val_acc = []
# 1. DEFINE OR LOAD MODEL / WEIGHTS
if (CONTINUE_TRAINING == False):
init_batch_nb = 0
model = Sequential()