def yolo_eval(graph,
yolo_outputs,
image_shape,
max_boxes=10,
score_threshold=.6,
iou_threshold=.5):
with graph.as_default():
box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs
boxes = yolo_boxes_to_corners(graph, box_xy, box_wh)
boxes, scores, classes = yolo_filter_boxes(graph,
boxes,
box_confidence,
box_class_probs,
threshold=score_threshold)
# Scale boxes back to original image shape.
height = image_shape[0]
width = image_shape[1]
image_dims = K.stack([height, width, height, width])
image_dims = K.reshape(image_dims, [1, 4])
boxes = boxes * image_dims
# TODO: Something must be done about this ugly hack!
max_boxes_tensor = K.variable(max_boxes, dtype='int32')
K.get_session().run(tf.variables_initializer([max_boxes_tensor]))
nms_index = tf.image.non_max_suppression(boxes,
scores,
max_boxes_tensor,
iou_threshold=iou_threshold)
boxes = K.gather(boxes, nms_index)
scores = K.gather(scores, nms_index)
classes = K.gather(classes, nms_index)
return boxes, scores, classes
def deactive_train(json_str):
data = json_str
if data == 'deactive_train':
######################
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del my_detector
del multipeople_classifier
del multiperson_tracker
except:
pass
print(gc.collect(
)) # if it's done something you should see a number being outputted
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
######################
print(60 * 'k')
repliesmess = "done_deactive"
print(repliesmess)
socketio.emit('done_deactive_train', data=repliesmess)
def reset_keras(model):
"""
Resets keras session
Parameters
----------
model: Model to clear
Returns
-------
"""
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del model # this is from global space - change this as you need
except:
pass
# use the same config as you used to create the session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
set_session(
sess) # set this TensorFlow session as the default session for Keras
def reset_keras():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del classifier # this is from global space - change this as you need
except:
pass
#print(gc.collect()) # if it's done something you should see a number being outputted
###################################
# TensorFlow wizardry
config = tensorflow.ConfigProto()
# Don't pre-allocate memory; allocate as-needed
config.gpu_options.allow_growth = True
# Only allow a total of half the GPU memory to be allocated
#config.gpu_options.per_process_gpu_memory_fraction = 0.5
# Create a session with the above options specified.
K.tensorflow_backend.set_session(tensorflow.Session(config=config))
print("available gpu divice: {}".format(tensorflow.test.gpu_device_name()))
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def reset_keras():
"""Resets a Keras session and clears memory."""
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del network # this is from global space - change this as you need
except:
pass
try:
del network_model # this is from global space - change this as you need
except:
pass
print(gc.collect()
) # if it's done something you should see a number being outputted
# use the same config as you used to create the session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = gpu_memory
config.gpu_options.visible_device_list = '0'
set_session(tf.Session(config=config))
def reset_keras():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
print("Keras backend has been reset")
def reset_keras(model):
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del model # this is from global space - change this as you need
except:
pass
print(gc.collect()) # if it's done something you should see a number being outputted
def reset_keras_tf_session():
"""
this function clears the gpu memory and set the
tf session to not use the whole gpu
"""
sess = get_session()
clear_session()
sess.close()
sess = get_session()
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tensorflow.Session(config=config))
def reset_keras():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
print(gc.collect()
) # if it's done something you should see a number being outputted
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def reset_keras(self):
print("Restarting Keras Session...")
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del model
except:
pass
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
print('Done!')
def clearGPUMemory():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
print("Garbage collected:", gc.collect())
cuda.select_device(0)
cuda.close()
def reset_keras():
import tensorflow
from keras.backend.tensorflow_backend import set_session
from keras.backend.tensorflow_backend import clear_session
from keras.backend.tensorflow_backend import get_session
sess = get_session()
clear_session()
sess.close()
sess = get_session()
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def cnn_test_color(self, trigger_type, gain, exp, classnamelist, cvv):
# ニュートラルネットワークで使用するモデル作成
model_filename = 'cnn_model.json'
weights_filename = 'cnn_weights.hdf5'
old_session = KTF.get_session()
#show_infrared_camera = None
#show_infrared_camera = ShowInfraredCamera()
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
json_string = open(os.path.join(self.f_model,
model_filename)).read()
model = model_from_json(json_string)
model.summary()
adam = keras.optimizers.Adam(lr=self.learning_rate)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.load_weights(os.path.join(self.f_model, weights_filename))
cvv.realtime_identification_color(classnamelist, model,
trigger_type, gain, exp,
self.im_size_width,
self.im_size_height, self.flip)
cbks = []
KTF.set_session(old_session)
def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
self.boxes, self.scores, self.classes = self.generate()
def reset_keras():
sess = get_session()
clear_session()
sess.close()
# if it's done something you should see a number being outputted
print("\nGarbage Collector: ", gc.collect())
def load_config(self):
# read model config from environment
self.device_str = os.environ.get("device_id", "/cpu:0")
self.user_config = tf.ConfigProto(allow_soft_placement=False)
gpu_mem_limit = float(os.environ.get("gpu_mem_limit", 0.3))
self.user_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_limit
self.user_config.gpu_options.allow_growth = True
if os.environ.get("log_device_placement", False):
self.user_config.log_device_placement = True
print("device id %s, gpu memory limit: %f" %
(self.device_str, gpu_mem_limit))
self.graph = tf.Graph()
with self.graph.as_default():
with tf.device(self.device_str):
self.session = tf.Session(config=self.user_config,
graph=self.graph)
KTF.set_session(self.session)
self.model = KerasXception(weights=self.weight,
input_shape=(self.input_shape[0],
self.input_shape[1],
self.input_shape[2]),
pooling=self.pooling,
include_top=False)
self.graph = KTF.get_graph()
self.session = KTF.get_session()
self.model.trainable = False
self.model.predict(
np.zeros((1, self.input_shape[0], self.input_shape[1], 3)))
self.graph.as_default()
self.session.as_default()
def OCR(image_path, typeP, attribute, thresholding=160):
"""
用来连接OCR调用，通过home/views.py来预加载全局模型
imgae_path 输入图片路径，识别图片为行提取结果
"""
time11 = time.time()
#global verify_global_model
#global global_model
global tax_model
with K.get_session().graph.as_default():
if typeP == 'normal' and attribute == 'verifyCode':
print('model: 3_global_model')
#out, _ = veryVat.predict(image_path, verify_global_model)
out = tax.predict(image_path, tax_model)
elif typeP == 'train':
out = predict.predict_single(image_path)
else:
print('model: global_model')
#out, _ = ocrVat.predict(image_path, global_model)
out = tax.predict(image_path, tax_model)
time12 = time.time()
print(attribute + ' 识别耗时： ' + str(time12 - time11))
return out
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
tf.histogram_summary(weight.name, weight)
if self.write_images:
w_img = tf.squeeze(weight)
shape = w_img.get_shape()
if len(shape) > 1 and shape[0] > shape[1]:
w_img = tf.transpose(w_img)
if len(shape) == 1:
w_img = tf.expand_dims(w_img, 0)
w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
tf.image_summary(weight.name, w_img)
if hasattr(layer, 'output'):
tf.histogram_summary('{}_out'.format(layer.name),
layer.output)
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and not self.merged:
mod_type = self.model.get_config()['name']
if mod_type == 'Sequential':
layers = {l.get_config()['name']: l for l in self.model.layers}
elif mod_type == 'Graph':
layers = self.model.nodes
else:
raise Exception('Unrecognized model:',
self.model.get_config()['name'])
for l in layers:
cur_layer = layers[l]
if hasattr(cur_layer, 'W'):
tf.histogram_summary('{}_W'.format(l), cur_layer.W)
if hasattr(cur_layer, 'b'):
tf.histogram_summary('{}_b'.format(l), cur_layer.b)
if hasattr(cur_layer, 'get_output'):
tf.histogram_summary('{}_out'.format(l),
cur_layer.get_output())
self.merged = tf.merge_all_summaries()
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
layers = self.model.layers
for layer in layers:
if hasattr(layer, 'W'):
tf.histogram_summary('{}_W'.format(layer), layer.W)
if hasattr(layer, 'b'):
tf.histogram_summary('{}_b'.format(layer), layer.b)
if hasattr(layer, 'output'):
tf.histogram_summary('{}_out'.format(layer), layer.output)
self.merged = tf.merge_all_summaries()
if self.write_graph:
if tf.__version__ >= '0.8.0':
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and not self.merged:
mod_type = self.model.get_config()['name']
if mod_type == 'Sequential':
layers = {l.get_config()['name']: l for l in self.model.layers}
elif mod_type == 'Graph':
layers = self.model.nodes
else:
raise Exception('Unrecognized model:',
self.model.get_config()['name'])
for l in layers:
cur_layer = layers[l]
if hasattr(cur_layer, 'W'):
tf.histogram_summary('{}_W'.format(l), cur_layer.W)
if hasattr(cur_layer, 'b'):
tf.histogram_summary('{}_b'.format(l), cur_layer.b)
if hasattr(cur_layer, 'get_output'):
tf.histogram_summary('{}_out'.format(l),
cur_layer.get_output())
self.merged = tf.merge_all_summaries()
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
def reset_keras():
"""
Releases keras session
"""
sess = get_session()
clear_session()
sess.close()
def train():
X = np.load('./data/train_X.npy')
y = np.load('./data/train_y.npy')
train_datagen = ImageDataGenerator(rescale=1.0 / 255.)
validation_datagen = ImageDataGenerator(rescale=1.0 / 255.)
train_generator = train_datagen.flow(X, y, batch_size=300)
validation_generator = validation_datagen.flow(X, y)
nb_epoch = 2000
nb_train_samples = N
nb_validation_samples = 600
old_session = KTF.get_session()
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
KTF.set_learning_phase(1)
model = Sequential()
with tf.name_scope("inference") as scope:
model = set_model(model)
model.summary()
fpath = './model/weights.hdf5'
tb_cb = TensorBoard(log_dir="./tensorlog", histogram_freq=1)
cp_cb = ModelCheckpoint(filepath=fpath,
monitor='val_loss',
verbose=1,
save_best_only=False,
mode='auto',
save_weights_only=True)
history = model.fit_generator(train_generator,samples_per_epoch=nb_train_samples, \
nb_epoch=nb_epoch, validation_data=validation_generator, \
nb_val_samples=nb_validation_samples,\
callbacks=[cp_cb, tb_cb])
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
layers = self.model.layers
for layer in layers:
if hasattr(layer, 'W'):
tf.histogram_summary('{}_W'.format(layer), layer.W)
if hasattr(layer, 'b'):
tf.histogram_summary('{}_b'.format(layer), layer.b)
if hasattr(layer, 'output'):
tf.histogram_summary('{}_out'.format(layer),
layer.output)
self.merged = tf.merge_all_summaries()
if self.write_graph:
if parse_version(tf.__version__) >= parse_version('0.8.0'):
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def __init__(self, yolo, **cfgs):
self.yolo = yolo
self.model_path = cfgs['model_path']
self.anchors_path = cfgs['anchors_path']
self.classes_path = cfgs['classes_path']
self.score_threshold = cfgs['score_threshold']
self.iou_threshold = cfgs['iou_threshold']
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.num_anchors = len(self.anchors)
self.num_classes = len(self.class_names)
#self.colors = self._colors()
if cfgs['cpu']:
config = tf.ConfigProto(device_count={"GPU": 0})
K.set_session(tf.Session(config=config))
self.sess = K.get_session()
self.model_image_size = (416, 416) # fixed size or (None, None), hw
self.input_image_shape = K.placeholder(shape=(2, ))
self.yolo_model = self._load_weights()
self.boxes, self.scores, self.classes = self.yolo.yolo_eval(
self.yolo_model.output,
self.anchors,
self.num_classes,
self.input_image_shape,
score_threshold=self.score_threshold,
iou_threshold=self.iou_threshold)
def __init__(self,
enable_actions,
environment_name,
graves=False,
ddqn=False):
# parameters
self.name = os.path.splittext(os.path.basename(__file__))[0]
self.environment_name = environment_name
self.enable_actions = enable_actions
self.n_action = enable_actions
self.n_action = len(self.enable_actions)
self.minibatch_size = 32
self.replay_memory_size = 5000
self.learning_rate = 0.00025
self.discount_factor = 0.9
self.use_graves = graves
self.use_ddqn = ddqn
self.exploration = INITIAL_EXPLORATION
self.exploration_step = (INITIAL_EXPLORATION -
FINAL_EXPLORATION) / EXPLORATION_STEPS
self.model_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "models")
self.model_name = "{}.ckpt".format(self.environment_name)
self.old_session = KTF.get_session()
self.session = tf.Session("")
KTF.set_session(self.session)
# replay memory
self.D = deque(maxlen=self.replay_memory_size)
# variables
self.current_loss = 0.0
def saveTF1p6Model(protobuffer_name):
frozen_graph = freeze_session(
K.get_session(), output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph,
os.getcwd(),
protobuffer_name,
as_text=False)
print("Saved frozen model in ", protobuffer_name)
def reset_keras():
sess = get_session()
clear_session()
sess.close()
# use the same config as you used to create the session
config = tf.ConfigProto() #allow_soft_placement=True, log_device_placement=True)
set_session(tf.Session(config=config))
def reset_keras(classifier):
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del classifier # this is from global space - change this as you need
except:
pass
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def reset_keras():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del classifier # this is from global space - change this as you need
except:
pass
# print(gc.collect()) # if it's done something you should see a number being outputted
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def reset_keras():
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del model
except:
pass
_ = gc.collect()
# use the same config as you used to create the session
config = tensorflow.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.visible_device_list = "0"
set_session(tensorflow.Session(config=config))
def reset_keras(self):
torch.cuda.empty_cache()
sess = get_session()
clear_session()
sess.close()
sess = get_session()
try:
del classifier
except:
pass
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1.0
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config))
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
tf.histogram_summary(weight.name, weight)
if self.write_images:
w_img = tf.squeeze(weight)
shape = w_img.get_shape()
if len(shape) > 1 and shape[0] > shape[1]:
w_img = tf.transpose(w_img)
if len(shape) == 1:
w_img = tf.expand_dims(w_img, 0)
w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
tf.image_summary(weight.name, w_img)
if hasattr(layer, 'output'):
tf.histogram_summary('{}_out'.format(layer.name),
layer.output)
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.merged = tf.summary.merge_all()
else:
self.merged = tf.merge_all_summaries()
if self.write_graph:
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
elif parse_version(tf.__version__) >= parse_version('0.8.0'):
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
if parse_version(tf.__version__) >= parse_version('0.12.0'):
self.writer = tf.summary.FileWriter(self.log_dir)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def train(self, ep=2000,noise_level=.01):
gan_feed = self.gan()
sess = K.get_session()
np.random.shuffle(self.x_train)
shuffled_cifar = self.x_train
length = len(shuffled_cifar)
for i in range(ep):
j = i % int(length / self.batch_size)
minibatch = shuffled_cifar[j*self.batch_size:(j+1)*self.batch_size]
z_input = np.random.normal(loc=0.,scale=1.,size=(self.batch_size,self.zed))
# train for one step
losses = gan_feed(sess,minibatch,z_input)
if (i % 10 == 0):
print('{0} - dloss:{1:6.4f} gloss:{2:6.4f}'.format(i,losses[0],losses[1]))
self.show()
def __call__(self, inputs):
if not isinstance(inputs, (list, tuple)):
raise TypeError('`inputs` should be a list or tuple.')
feed_dict = {}
for tensor, value in zip(self.inputs, inputs):
if is_sparse(tensor):
sparse_coo = value.tocoo()
indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
np.expand_dims(sparse_coo.col, 1)),
1)
value = (indices, sparse_coo.data, sparse_coo.shape)
feed_dict[tensor] = value
session = get_session()
enqueue_ops = self._enqueue_ops
neops = len(enqueue_ops)
updated = session.run(enqueue_ops + self.outputs + [self.updates_op],
feed_dict=feed_dict,
**self.session_kwargs)
nouts = len(self.outputs)
# return updated[:len(self.outputs)]
return updated[neops:nouts + neops]
def _set_model(self, model):
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
self.model = model
self.sess = KTF.get_session()
if self.histogram_freq and self.merged is None:
layers = self.model.layers
for layer in layers:
if hasattr(layer, "W"):
tf.histogram_summary("{}_W".format(layer), layer.W)
if hasattr(layer, "b"):
tf.histogram_summary("{}_b".format(layer), layer.b)
if hasattr(layer, "output"):
tf.histogram_summary("{}_out".format(layer), layer.output)
self.merged = tf.merge_all_summaries()
if self.write_graph:
if tf.__version__ >= "0.8.0":
self.writer = tf.train.SummaryWriter(self.log_dir, self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir, self.sess.graph_def)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def test_TensorBoard():
import shutil
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
old_session = KTF.get_session()
filepath = './logs'
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
nb_test=test_samples,
input_shape=(input_dim,),
classification=True,
nb_class=nb_class)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
def data_generator(train):
if train:
max_batch_index = len(X_train) // batch_size
else:
max_batch_index = len(X_test) // batch_size
i = 0
while 1:
if train:
yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size])
else:
yield (X_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size])
i += 1
i = i % max_batch_index
def data_generator_graph(train):
while 1:
if train:
yield {'X_vars': X_train, 'output': y_train}
else:
yield {'X_vars': X_test, 'output': y_test}
# case 1 Sequential
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
# fit with validation data
model.fit(X_train, y_train, batch_size=batch_size,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
# fit with validation data and accuracy
model.fit(X_train, y_train, batch_size=batch_size,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2)
# fit generator with validation data
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
validation_data=(X_test, y_test),
callbacks=cbks)
# fit generator without validation data
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
callbacks=cbks)
# fit generator with validation data and accuracy
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
validation_data=(X_test, y_test),
callbacks=cbks)
# fit generator without validation data and accuracy
model.fit_generator(data_generator(True), len(X_train), nb_epoch=2,
callbacks=cbks)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
# case 2 Graph
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
model = Graph()
model.add_input(name='X_vars', input_shape=(input_dim,))
model.add_node(Dense(nb_hidden, activation="sigmoid"),
name='Dense1', input='X_vars')
model.add_node(Dense(nb_class, activation="softmax"),
name='last_dense',
input='Dense1')
model.add_output(name='output', input='last_dense')
model.compile(optimizer='sgd', loss={'output': 'mse'})
tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1)
cbks = [tsb]
# fit with validation
model.fit({'X_vars': X_train, 'output': y_train},
batch_size=batch_size,
validation_data={'X_vars': X_test, 'output': y_test},
callbacks=cbks, nb_epoch=2)
# fit wo validation
model.fit({'X_vars': X_train, 'output': y_train},
batch_size=batch_size,
callbacks=cbks, nb_epoch=2)
# fit generator with validation
model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2,
validation_data={'X_vars': X_test, 'output': y_test},
callbacks=cbks)
# fit generator wo validation
model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2,
callbacks=cbks)
assert os.path.exists(filepath)
shutil.rmtree(filepath)
KTF.set_session(old_session)
