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import sys | |
import numpy | |
import os | |
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' | |
numpy.set_printoptions(threshold=sys.maxsize) | |
import torch | |
from torch.distributions import normal | |
import seaborn as sns; |
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def binary_data(n): | |
b = [] | |
for i in range(1 << n): | |
s = bin(i)[2:] | |
s = '0' * (n - len(s)) + s | |
b.append(map(int, list(s))) | |
return b |
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from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation | |
from keras import losses | |
import numpy as np | |
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) | |
y = np.array([[0], [1], [1], [0]]) | |
model = Sequential() |
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from __future__ import absolute_import, division, print_function, unicode_literals | |
import tensorflow as tf | |
import numpy as np | |
from tensorflow import keras | |
from tensorflow.keras import layers | |
inputs = keras.Input(shape=(784,), name='digits') |
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from keras.datasets import mnist | |
from keras.layers import Input, Dense | |
from keras.models import Model | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
(X_train, _), (X_test, _) = mnist.load_data() | |
X_train = X_train.astype('float32')/255 | |
X_test = X_test.astype('float32')/255 |
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import os | |
import pandas as pd | |
import mysql.connector | |
from sqlalchemy import create_engine | |
# ====== Connection ====== # | |
# Connecting to mysql by providing a sqlachemy engine | |
engine = create_engine('mysql+mysqlconnector://myhu:password@192.168.68.102/dbname', echo=True) |
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import keras | |
from keras.datasets import mnist | |
from keras.layers import Input, Dense | |
from keras.models import load_model | |
from skimage.util import invert | |
import numpy as np | |
import matplotlib.pyplot as plt | |
(X_train, y_train), (X_test, y_test) = mnist.load_data() |
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import sys | |
from flask import Flask, jsonify, request | |
from keras.datasets import mnist | |
import MySQLdb | |
app = Flask(__name__) | |
@app.route("/", methods=["GET", "POST"]) | |
def hello(): |
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import os | |
from datetime import datetime | |
import coloredlogs, logging | |
import tensorflow as tf | |
import tensorflow.python.util.deprecation as deprecation | |
tf.debugging.set_log_device_placement(True) | |
coloredlogs.install() |
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print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) | |
#input image is 5 X 5 and 1 channel | |
input_shape = (1, 1, 5, 5) | |
tf.summary.trace_on(graph=True, profiler=True) | |
x = tf.random.normal(input_shape) | |
print(x.shape) | |
y = tf.keras.layers.Conv2D( |