rish-16

def rand_int():
    new_job = qk.execute(circ, backend, shots=1)
    
    # The output bitstring consists of 3 collapsed Qubits (bits)
    bitstring = new_job.results().get_counts()
    bitstring = list(bitstring.keys()[0])
    
    # Converting binary to Decimal integers
    random_integer = int(bitstring, 2)
    

rish-16

import qiskit as qk

# Load saved account from memory
qk.IBMQ.load_accounts()

n = 3
q = qk.QuantumRegister(n)
c = qk.ClassicalRegister(n)
circ = qk.QuantumCircuit(q, c)

rish-16

a = rand_int()
print (a)

rish-16

#!/usr/bin/env python3

import os
from flask import Flask, request
from interactive_conditional_samples import interact_model
app = Flask(__name__)

@app.route('/', methods=['GET'])
def hello():
    return "Hello from GPT-2 model server!"

rish-16

# Here, we have added a `raw_text` parameter
# From our Flask API we'll pass in the incoming text into this function
def interact_model(raw_text, model_name='117M', seed=None, nsamples=1, batch_size=1, length=None, temperature=1, top_k=40, models_dir='models/'):
    models_dir = os.path.expanduser(os.path.expandvars(models_dir))
    if batch_size is None:
        batch_size = 1
    assert nsamples % batch_size == 0

    enc = encoder.get_encoder(model_name, models_dir)
    hparams = model.default_hparams()

rish-16

# Detect hardware
try:
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
except ValueError: # If TPU not found, resolve to GPU instead
  tpu = None
  gpus = tf.config.experimental.list_logical_devices("GPU")
    
# Select appropriate distribution strategy
if tpu:
	tf.tpu.experimental.initialize_tpu_system(tpu)

rish-16

resolver = tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.contrib.distribute.initialize_tpu_system(resolver)
strategy = tf.contrib.distribute.TPUStrategy(resolver)

rish-16

with strategy.scope():
  """
  This essentailly takes our model and makes it 
  compatible to train on a TPU.
  """
  model = tf.keras.models.Sequential()
  model.add(tf.keras.layers.Dense(512, input_size=[784,], activation='relu'))
  model.add(tf.keras.layers.Dense(256, activation='relu'))
  model.add(tf.keras.layers.Dense(128, activation='relu'))
  model.add(tf.keras.layers.Dense(64, activation='relu'))

rish-16

history = model.fit(x_train, 
		    y_train,
                    epochs=20,
                    steps_per_epoch=50
                    )

model.save_weights('./mnist_model.h5', overwrite=True)

rish-16

# Detect hardware
try:
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
except ValueError: # If TPU not found
  tpu = None