FavioVazquez

FavioVazquez

from keras.datasets import mnist
from autokeras.classifier import ImageClassifier

if __name__ == '__main__':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(x_train.shape + (1,))
    x_test = x_test.reshape(x_test.shape + (1,))

    clf = ImageClassifier(verbose=True, augment=False)
    clf.fit(x_train, y_train, time_limit=12 * 60 * 60)

FavioVazquez

from sparkdl import KerasTransformer
from keras.models import Sequential
from keras.layers import Dense
import numpy as np

# Generate random input data
num_features = 10
num_examples = 100
input_data = [{"features" : np.random.randn(num_features).astype(float).tolist()} for i in range(num_examples)]
schema = StructType([ StructField("features", ArrayType(FloatType()), True)])

FavioVazquez

FavioVazquez

FavioVazquez

FavioVazquez

FavioVazquez

FavioVazquez

FavioVazquez

import numpy as np
import matplotlib.pyplot as plt

def softmax(z):
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z), axis=1))
    return sm


def initialize(dim1, dim2):