c = tfg.Variable(orange_price, name="c")
d = tfg.Variable(orange_num, name="d")
e = tfg.Variable(tax, name="e")
# Create Mul operation node
f = tfg.Mul(a, b, name="f")
g = tfg.Mul(c, d, name="g")
# Create Add operation node
h = tfg.Add(f, g, name="h")
# Create Mul operation node
i = tfg.Mul(h, e, name="i")
session = tfs.Session()
# forward
final_price = session.run(i, verbose=False)
print("final_price: {:f}".format(float(final_price)))
print()
#backward
d_in = 1
d_total_price, d_tax = i.backward(d_in)
print("d_total_price: {:f}".format(float(d_total_price)))
print("d_tax: {:f}".format(float(d_tax)))
d_total_apple_price, d_total_orange_price = h.backward(d_total_price)
print("d_total_apple_price: {:f}".format(float(d_total_apple_price)))
print("d_total_orange_price: {:f}".format(float(d_total_orange_price)))
def __init__(self,
input_dim,
cnn_param_list,
fc_hidden_size,
output_size,
input_node=None,
target_node=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.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.shape_before_fc = None
self.num_neurons_flatten_for_fc = None
self.initialize_param(sd=init_sd)
self.layering()