def train(self, save_path, name, cross_validation=True, reshape=None):
history = History(save_path, name)
fold_index = 0
for train_dataset, test_dataset in self.feed.take(
self.n_splits if cross_validation else 1):
# train_dataset是训练集，test_dataset是测试集。train_dataset[0]和test_dataset[0]是data，四维数组；train[1]和test[1]是label，二维数组。
fold_index += 1
pca_model = PCA(n_components=self.pca_components)
lda_model = LinearDiscriminantAnalysis()
train_data = numpy.array(train_dataset[0])
train_label = numpy.array(train_dataset[1])
test_data = numpy.array(test_dataset[0])
test_label = numpy.array(test_dataset[1])
pca_model.fit(train_data.reshape((train_data.shape[0], -1)))
train_pc = pca_model.transform(
train_data.reshape((train_data.shape[0], -1)))
lda_model.fit(train_pc, train_label)
# model.load_weights(os.path.join(save_path, 'test_weights.h5'))
r = {}
r['acc'] = lda_model.score(train_pc, train_label)
print('acc = ' + str(r['acc']))
test_pc = pca_model.transform(
test_data.reshape((test_data.shape[0], -1)))
r['val_acc'] = lda_model.score(test_pc, test_label)
print('val_acc = ' + str(r['val_acc']))
history.add(str(fold_index), r)
# Save weights to a HDF5 file
# self.model.save(self.save_path)
history.save()
def train(self, save_path, name, cross_validation=True, reshape=None):
history = History(save_path, name)
fold_index = 0
for train_dataset, test_dataset in self.feed.take(
self.n_splits if cross_validation else 1):
# train_dataset是训练集，test_dataset是测试集。train_dataset[0]和test_dataset[0]是data，四维数组；train[1]和test[1]是label，二维数组。
fold_index += 1
svm_model = SVC()
train_data = numpy.array(train_dataset[0])
train_label = numpy.array(train_dataset[1])
test_data = numpy.array(test_dataset[0])
test_label = numpy.array(test_dataset[1])
svm_model.fit(train_data.reshape((train_data.shape[0], -1)),
train_label)
# model.load_weights(os.path.join(save_path, 'test_weights.h5'))
r = {}
r['acc'] = svm_model.score(
train_data.reshape((train_data.shape[0], -1)), train_label)
print('acc = ' + str(r['acc']))
r['val_acc'] = svm_model.score(
test_data.reshape((train_data.shape[0], -1)), test_label)
print('val_acc = ' + str(r['val_acc']))
history.add(str(fold_index), r)
# Save weights to a HDF5 file
# self.model.save(self.save_path)
history.save()
def train(self, save_path, name, cross_validation=True):
history = History(save_path, name)
fold_index = 0
if version.parse(tf.__version__) < version.parse('1.13'):
self.compile(optimizer=tf.train.AdamOptimizer(
learning_rate=self.learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy'])
else:
self.compile(
optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy'])
for train_dataset, test_dataset in self.feed.take(
self.n_splits if cross_validation else 1):
# train_dataset是训练集，test_dataset是测试集。train_dataset[0]和test_dataset[0]是data，四维数组；train[1]和test[1]是label，二维数组。
fold_index += 1
checkpoint_file = os.path.join(
save_path, 'cp_' + '{:02d}'.format(fold_index) +
'{epoch:03d}_{val_loss:.4f}.h5')
train_dataset = tf.data.Dataset.from_tensors(
train_dataset).repeat().shuffle(1024).apply(
tf.data.experimental.unbatch()).batch(self.batch_size)
test_dataset = tf.data.Dataset.from_tensors(
test_dataset).repeat().shuffle(1024).apply(
tf.data.experimental.unbatch()).batch(self.batch_size)
print('lr = ' + str(self.learning_rate) + '')
print('batch_size = ' + str(self.batch_size) + '')
ckpt = tf.keras.callbacks.ModelCheckpoint(checkpoint_file,
verbose=1,
save_weights_only=True,
period=1)
# model.load_weights(os.path.join(save_path, 'test_weights.h5'))
hist = self.fit(
train_dataset,
epochs=self.training_epoch,
callbacks=[
ckpt,
tf.keras.callbacks.TensorBoard(log_dir=save_path)
],
steps_per_epoch=self.epoch_steps,
validation_data=test_dataset,
validation_steps=self.test_steps)
history.add(str(fold_index), dict(hist.history))
# Save weights to a HDF5 file
self.save(save_path)
history.save()