Datasets:
celinelee
/
python_fn_calls

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self.params.items()
k.startswith('-')
log.warning('Unrecognized long argument passed to LSF CLI plugin: %s' % k)
scriptargs.items()
dict(headers=template_scriptargs)
submit(self, script_file)
delete(self, job_id)
get_status(self, job_ids=None)
get_single_status(self, job_id)
parse_status(self, status, job_ids)
status.splitlines()
line.split()
self._get_job_state(state)
parse_single_status(self, status, job_id)
log.warning("Job id '%s' not found LSF status check" % job_id)
self._get_job_state(status)
get_failure_reason(self, job_id)
parse_failure_reason(self, reason, job_id)
reason.splitlines()
_get_job_state(self, state)
get(state)
KeyError("Failed to map LSF status code [%s] to job state." % state)
get_weight_path()
os.path.join("pretrain", 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5')
get_img_output_length(width, height)
get_output_length(input_length)
get_output_length(width)
get_output_length(height)
dense_block(x, blocks, name)
range(blocks)
conv_block(x, 32, name=name + '_block' + str(i + 1)
transition_block(x, reduction, name)
K.image_data_format()
layers.Activation('relu', name=name + '_relu')
layers.Conv2D(int(K.int_shape(x)
layers.AveragePooling2D(2, strides=2, name=name + '_pool', padding='same')
conv_block(x, growth_rate, name)
K.image_data_format()
layers.Activation('relu', name=name + '_0_relu')
layers.Activation('relu', name=name + '_1_relu')
layers.Concatenate(axis=bn_axis, name=name + '_concat')
tensor (i.e. output of `layers.Input()
not (weights in {'imagenet', None} or os.path.exists(weights)
K.image_dim_ordering()
Input(shape=input_shape)
K.is_keras_tensor(input_tensor)
Input(tensor=input_tensor, shape=input_shape)
K.image_dim_ordering()
ZeroPadding2D((3, 3)
layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')
layers.Activation('relu', name='conv1/relu')
layers.ZeroPadding2D(padding=((1, 1)
layers.MaxPooling2D(3, strides=2, name='pool1')
dense_block(x, blocks[0], name='conv2')
transition_block(x, 0.5, name='pool2')
dense_block(x, blocks[1], name='conv3')
transition_block(x, 0.5, name='pool3')
dense_block(x, blocks[2], name='conv4')
transition_block(x, 0.5, name='pool4')
dense_block(x, blocks[3], name='conv5')
layers.Activation('relu', name='relu')
rpn(base_layers,num_anchors)
Convolution2D(512, (3, 3)
Convolution2D(num_anchors, (1, 1)
Convolution2D(num_anchors * 4, (1, 1)
classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False)
K.backend()
K.backend()
RoiPoolingConv(pooling_regions, num_rois)
TimeDistributed(AveragePooling2D((7, 7)
TimeDistributed(Flatten(name='flatten')
TimeDistributed(Dense(4096, activation='relu', name='fc1')
TimeDistributed(Dropout(0.5)
TimeDistributed(Dense(4096, activation='relu', name='fc2')
TimeDistributed(Dropout(0.5)
TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero')
format(nb_classes)
TimeDistributed(Dense(4 * (nb_classes-1)
format(nb_classes)
gevent.monkey.patch_all()
flask.Flask(__name__, template_folder='.')
app.route('/')
index()
flask.render_template('main.html.tpl', config=config)
app.route('/users')
list_users()
requests.get('https://chaturbate.com/')
re.findall(r'<div class="details">\s*<div class="title">\s*<a\s*href=\s*"/(.+?)
gevent.pool.Pool(10)
pool.map(get_user_stream, user_names)
zip(user_names, stream_urls)
flask.Response(json.dumps(users)
get_user_stream(user)
requests.get('https://chaturbate.com/{}/?use_html_chat=1'.format(user)
re.search('<video .*src="(.+?)
match.group(1)
replace(' ', '%20')
urllib.parse.urlparse(playlist)
re.sub('[^\d]', '', playlist.netloc.split('.')
format(server_number, playlist.path)