giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

from scipy.integrate import odeint

# Set random seed (for reproducibility)
np.random.seed(1000)

# Start and end time (in milliseconds)
tmin = 0.0

giuseppebonaccorso

import numpy as np

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

# Set random seed for reproducibility
np.random.seed(1000)

# Create and scale dataset
X, _ = make_blobs(n_samples=500, centers=2, cluster_std=5.0, random_state=1000)

giuseppebonaccorso

import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import tensorflow as tf

from keras.datasets import cifar10

# Set random seed (for reproducibility)
np.random.seed(1000)
tf.set_random_seed(1000)

giuseppebonaccorso

'''
OpenAI-Gym Cartpole-v0 LSTM experiment
Giuseppe Bonaccorso (http://www.bonaccorso.eu)
'''

import gym
import numpy as np
import time

from keras.models import Sequential

giuseppebonaccorso

import numpy as np
import tensorflow as tf

# Set random seed for reproducibility
np.random.seed(1000)

nb_users = 5000
nb_products = 2000
nb_factors = 500
max_rating = 5

giuseppebonaccorso

from sklearn.datasets import make_classification

import numpy as np

# Set random seed (for reproducibility)
np.random.seed(1000)

def sigmoid(arg):
    return 1.0 / (1.0 + np.exp(-arg))

giuseppebonaccorso

import numpy as np
import tensorflow as tf

from sklearn.datasets import make_blobs

# Set random seed (for reproducibility)
np.random.seed(1000)

# Create dataset
nb_samples=2000

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

# Set random seed for reproducibility
np.random.seed(1000)

nb_patterns = 4
pattern_width = 4
pattern_height = 4
max_iterations = 10

giuseppebonaccorso

import keras.backend as K
import multiprocessing
import tensorflow as tf

from gensim.models.word2vec import Word2Vec

from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv1D

giuseppebonaccorso

'''
Cifar-10 classification
Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information
See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information
'''
 
from __future__ import print_function

import numpy as np