mxnet-digits-test.py

import mxnet as mx
import numpy as np
import cv2
import logging

logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

# Variables are place holders for input arrays. We give each variable a unique name.
data = mx.symbol.Variable('data')

# The input is fed to a fully connected layer that computes Y=WX+b.
# This is the main computation module in the network.
# Each layer also needs an unique name. We'll talk more about naming in the next section.
fc1  = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
# Activation layers apply a non-linear function on the previous layer's output.
# Here we use Rectified Linear Unit (ReLU) that computes Y = max(X, 0).
act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")

fc2  = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")

fc3  = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
# Finally we have a loss layer that compares the network's output with label and generates gradient signals.
mlp  = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')

# mx.viz.plot_network(mlp)


mlp.list_arguments()

from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
np.random.seed(1234) # set seed for deterministic ordering
p = np.random.permutation(mnist.data.shape[0])
X = mnist.data[p]
Y = mnist.target[p]

# for i in range(10):
#     plt.subplot(1,10,i+1)
#     plt.imshow(X[i].reshape((28,28)), cmap='Greys_r')
#     plt.axis('off')
# plt.show()

X = X.astype(np.float32)/255
X_train = X[:60000]
X_test = X[60000:]
Y_train = Y[:60000]
Y_test = Y[60000:]


batch_size = 100
train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size)
test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=batch_size)


model = mx.model.FeedForward(
    ctx = mx.cpu(),      # Run on GPU 0
    symbol = mlp,         # Use the network we just defined
    num_epoch = 10,       # Train for 10 epochs
    learning_rate = 0.1,  # Learning rate
    momentum = 0.9,       # Momentum for SGD with momentum
    wd = 0.00001)         # Weight decay for regularization
model.fit(
    X=train_iter,  # Training data set
    eval_data=test_iter,  # Testing data set. MXNet computes scores on test set every epoch
    batch_end_callback = mx.callback.Speedometer(batch_size, 200))  # Logging module to print out progress

print model.predict(X_test[0:1])[0].argmax()
print 'Accuracy:', model.score(test_iter)*100, '%'



from yhat import Yhat, YhatModel


class MxNetModel(YhatModel):
    def execute(self, data):
        img_list = data['image']
        img = np.array([img_list])
        return { "guess": model.predict(img)[0].argmax() }


testcase = { 'image': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 159, 253, 159, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 238, 252, 252, 252, 237, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 54, 227, 253, 252, 239, 233, 252, 57, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 60, 224, 252, 253, 252, 202, 84, 252, 253, 122, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 163, 252, 252, 252, 253, 252, 252, 96, 189, 253, 167, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 238, 253, 253, 190, 114, 253, 228, 47, 79, 255, 168, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 48, 238, 252, 252, 179, 12, 75, 121, 21, 0, 0, 253, 243, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 165, 253, 233, 208, 84, 0, 0, 0, 0, 0, 0, 253, 252, 165, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 178, 252, 240, 71, 19, 28, 0, 0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 57, 252, 252, 63, 0, 0, 0, 0, 0, 0, 0, 0, 0, 253, 252, 195, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 198, 253, 190, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 253, 196, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 76, 246, 252, 112, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 253, 252, 148, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 230, 25, 0, 0, 0, 0, 0, 0, 0, 0, 7, 135, 253, 186, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 223, 0, 0, 0, 0, 0, 0, 0, 0, 7, 131, 252, 225, 71, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 145, 0, 0, 0, 0, 0, 0, 0, 48, 165, 252, 173, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 86, 253, 225, 0, 0, 0, 0, 0, 0, 114, 238, 253, 162, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 249, 146, 48, 29, 85, 178, 225, 253, 223, 167, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 252, 252, 252, 229, 215, 252, 252, 252, 196, 130, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 199, 252, 252, 253, 252, 252, 233, 145, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 128, 252, 253, 252, 141, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}

print MxNetModel().execute(testcase)
yh = Yhat(USERNAME, APIKEY, URL)
print yh.deploy("MxNetExample", MxNetModel, globals(), sure=True)