def __init__(self, input_size, output_size, input_node, target_node,
initializer, activator, optimizer, learning_rate,
model_params_dir):
self.input_size = input_size
self.output_size = output_size
self.input_node = input_node
self.target_node = target_node
self.activator = activator
self.initializer = initializer
self.optimizer = optimizer(learning_rate=learning_rate)
self.params = {}
self.optimal_epoch_and_params = None
self.output = None
self.error = None
self.max_epoch = None
self.model_params_dir = model_params_dir
self.session = tfs.Session()
self.mode_id = ''.join(
random.choices(string.ascii_uppercase + string.digits, k=8))
super().__init__()
def __init__(self, input_size, output_size):
self.input_size = input_size
self.output_size = output_size
self.input_node = None
self.target_node = None
self.activator = None
self.initializer = None
self.optimizer = None
self.params = OrderedDict()
self.output = None
self.error = None
self.session = tfs.Session()
super().__init__()
def __init__(self,
input_size,
hidden_size_list,
output_size,
input_node=None,
target_node=None,
initializer=tfe.Initializer.Normal.value,
init_sd=0.01,
activator=tfe.Activator.ReLU.value,
optimizer=tfe.Optimizer.SGD.value,
learning_rate=0.01):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.input_node = input_node
self.target_node = target_node
self.activator = activator
self.initializer = initializer
self.optimizer = optimizer(learning_rate=learning_rate)
self.params = {}
self.optimal_epoch_and_params = None
self.output = None
self.error = None
self.max_epoch = None
self.session = tfs.Session()
self.mode_id = ''.join(
random.choices(string.ascii_uppercase + string.digits, k=8))
print("Multi Layer Network Model - ID:", self.mode_id)
self.hidden_size_list = hidden_size_list
self.hidden_layer_num = len(hidden_size_list)
self.params_size_list = None
self.layers = OrderedDict()
self.train_error_list = []
self.validation_error_list = []
self.test_accuracy_list = []
self.min_validation_error_epoch = sys.maxsize
self.min_train_error = sys.float_info.max
self.min_validation_error = sys.float_info.max
self.min_fold_idx = sys.maxsize
self.test_accuracy_at_min_validation_error_epoch = 0.0
self.min_validation_error_per_fold = []
self.param_mean_list = {}
self.param_variance_list = {}
self.param_skewness_list = {}
self.param_kurtosis_list = {}
self.output_mean_list = {}
self.output_variance_list = {}
self.output_skewness_list = {}
self.output_kurtosis_list = {}
self.initialize_param(sd=init_sd)
self.layering()
a = tfg.Variable(5.0, name='a')
b = tfg.Variable(1.0, name='b')
# Create placeholder
x = tfg.Placeholder(name='x')
# Create hidden node y
y = tfg.Mul(a, x, name="y")
# Create output node z
z = tfg.Add(y, b, name="z")
#nx.draw_networkx(g, with_labels=True)
#plt.show(block=True)
session = tfs.Session()
output = session.run(z, {x: 1.0})
print(output)
print(z.input_nodes[0], z.input_nodes[1])
print(z.output)
print(z.consumers)
print(y.consumers[0])
print(x.consumers[0])
print(a.consumers[0])
print(a.consumers)
# print(a.consumers[1]) 여러번 사용될경우 생김
print('***********************************')
# Create a new graph
def __init__(self,
input_dim,
cnn_param_list,
fc_hidden_size,
output_size,
input_node=None,
target_node=None,
use_batch_normalization=False,
use_dropout=False,
dropout_ratio_list=None,
conv_initializer=tfe.Initializer.Conv_Xavier_Normal,
initializer=tfe.Initializer.Normal.value,
init_sd=0.01,
activator=tfe.Activator.ReLU.value,
optimizer=tfe.Optimizer.SGD.value,
learning_rate=0.01):
super().__init__()
self.input_dim = input_dim
self.cnn_param_list = cnn_param_list
self.fc_hidden_size = fc_hidden_size
self.output_size = output_size
self.use_batch_normalization = use_batch_normalization
self.use_dropout = use_dropout
self.dropout_ratio_list = dropout_ratio_list
self.input_node = input_node
self.target_node = target_node
self.conv_initializer = conv_initializer
self.initializer = initializer
self.activator = activator
self.optimizer = optimizer(learning_rate=learning_rate)
self.params = {}
self.optimal_epoch_and_params = None
self.output = None
self.error = None
self.max_epoch = None
self.session = tfs.Session()
self.mode_id = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8))
print("Convolutional Neural Network Model - ID:", self.mode_id)
self.params_size_list = None
self.layers = OrderedDict()
self.train_error_list = []
self.validation_error_list = []
self.test_accuracy_list = []
self.min_validation_error_epoch = sys.maxsize
self.min_train_error = sys.float_info.max
self.min_validation_error = sys.float_info.max
self.min_fold_idx = sys.maxsize
self.test_accuracy_at_min_validation_error_epoch = 0.0
self.min_validation_error_per_fold = []
self.param_mean_list = {}
self.param_variance_list = {}
self.param_skewness_list = {}
self.param_kurtosis_list = {}
self.output_mean_list = {}
self.output_variance_list = {}
self.output_skewness_list = {}
self.output_kurtosis_list = {}
self.shape_before_fc = None
self.num_neurons_flatten_for_fc = None
self.last_layer_idx = -1
self.global_last_epoch = -1
self.param_idx_list = []
self.conv_param_idx_list = []
self.initialize_param(sd=init_sd)
self.layering()
