kfkd1.py

from lasagne.updates import nesterov_momentum
import cPickle as pickle
from datetime import datetime
import os
import sys

from matplotlib import pyplot
import numpy as np
from lasagne import layers
from nolearn.lasagne import BatchIterator
from nolearn.lasagne import NeuralNet
from pandas import DataFrame
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle
import theano

try:
    from lasagne.layers.cuda_convnet import Conv2DCCLayer as Conv2DLayer
    from lasagne.layers.cuda_convnet import MaxPool2DCCLayer as MaxPool2DLayer
except ImportError:
    Conv2DLayer = layers.Conv2DLayer
    MaxPool2DLayer = layers.MaxPool2DLayer


sys.setrecursionlimit(10000)  # for pickle...
np.random.seed(42)

FTRAIN = 'data/transtraining1.csv'
FTEST = 'data/test.csv'
FLOOKUP = 'data/IdLookupTable.csv'



def float32(k):
    return np.cast['float32'](k)


def load(test=False, cols=None):
    """Loads data from FTEST if *test* is True, otherwise from FTRAIN.
    Pass a list of *cols* if you're only interested in a subset of the
    target columns.
    """
    fname = FTEST if test else FTRAIN
    df = read_csv(os.path.expanduser(fname))  # load pandas dataframe

    # The Image column has pixel values separated by space; convert
    # the values to numpy arrays:
    df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))

    if cols:  # get a subset of columns
        df = df[list(cols) + ['Image']]

    print(df.count())  # prints the number of values for each column
    df = df.dropna()  # drop all rows that have missing values in them

    X = np.vstack(df['Image'].values) / 255.  # scale pixel values to [0, 1]
    X = X.astype(np.float32)

    if not test:  # only FTRAIN has any target columns
        y = df[df.columns[:-1]].values
        y = (y - 48) / 48  # scale target coordinates to [-1, 1]
        X, y = shuffle(X, y, random_state=42)  # shuffle train data
        y = y.astype(np.float32)
    else:
        y = None

    return X, y


def load2d(test=False, cols=None):
    X, y = load(test=test, cols=cols)
    X = X.reshape(-1, 1, 96, 96)
    return X, y


def plot_sample(x, y, axis):
    img = x.reshape(96, 96)
    axis.imshow(img, cmap='gray')
    if y is not None:
        axis.scatter(y[0::2] * 48 + 48, y[1::2] * 48 + 48, marker='x', s=10)


def plot_weights(weights):
    fig = pyplot.figure(figsize=(6, 6))
    fig.subplots_adjust(
        left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

    for i in range(16):
        ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])
        ax.imshow(weights[:, i].reshape(96, 96), cmap='gray')
    pyplot.show()


net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        ('input', layers.InputLayer),
        ('hidden', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(None, 9216),  # 96x96 input pixels per batch
    hidden_num_units=100,  # number of units in hidden layer
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=30,  # 30 target values

    # optimization method:
    update=nesterov_momentum,
    update_learning_rate=0.01,
    update_momentum=0.9,

    regression=True,  # flag to indicate we're dealing with regression problem
    max_epochs=400,  # we want to train this many epochs
    verbose=1,
    )



X, y = load()
net1.fit(X, y)

with open('netsinglehid.pickle', 'wb') as f:
    pickle.dump(net1, f, -1)

def plot_sample(x, y, axis):
    img = x.reshape(96, 96)
    axis.imshow(img, cmap='gray')
    axis.scatter(y[0::2] * 48 + 48, y[1::2] * 48 + 48, marker='x', s=10)

X, _ = load(test=True)
y_pred = net1.predict(X)

fig = pyplot.figure(figsize=(6, 6))
fig.subplots_adjust(
    left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

'''
for i in range(16):
    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])
    plot_sample(X[i], y_pred[i], ax)
    fig.savefig(str(i+) + '.jpg')
'''

train_loss = np.array([i["train_loss"] for i in net1.train_history_])
valid_loss = np.array([i["valid_loss"] for i in net1.train_history_])
pyplot.plot(train_loss, linewidth=3, label="train")
pyplot.plot(valid_loss, linewidth=3, label="valid")
pyplot.grid()
pyplot.legend()
pyplot.xlabel("epoch")
pyplot.ylabel("loss")
pyplot.ylim(1e-3, 1e-2)
pyplot.yscale("log")
pyplot.savefig("graphwithavgerror")