BrianDo2005

BrianDo2005

NEM NIS API Walkthrough

Table of Contents

• /heartbeat
• /status
• /chain/height
• /chain/score
• /account/get
• /namespace

BrianDo2005

import time
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten
from keras.layers import Conv2D, Conv2DTranspose
from keras.optimizers import RMSprop, from keras import regularizers

def init_model():
    start_time = time.time()
    print 'Compiling Model ... '
    model = Sequential()

BrianDo2005

BrianDo2005

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib.distributions import Bernoulli


class VariationalDense:
    """Variational Dense Layer Class"""
    def __init__(self, n_in, n_out, model_prob, model_lam):
        self.model_prob = model_prob

BrianDo2005

import numpy as np
from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist
import matplotlib.pyplot as plt

# this is the size of our encoded representations
encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats

# this is our input placeholder

BrianDo2005

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np
from keras.callbacks import TensorBoard
import matplotlib.pyplot as plt

input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format

BrianDo2005

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.callbacks import TensorBoard
from keras.datasets import mnist
import numpy as np
from keras import backend as K
import matplotlib.pyplot as plt

input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format

BrianDo2005

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

BrianDo2005

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