import os

import numpy as np
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle

from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet

def load(test=False, cols=None):
    fname = "path/to/train.csv"
    df = read_csv(os.path.expanduser(fname))


    df = df.dropna()

    X = np.array(np.vstack(df['Emotion'].values) , dtype=object)
    X = X.astype(np.chararray)

    df = df.drop('Emotion', 1)
    df = df.drop('Unnamed: 0', 1)

    y = np.array(df.values, dtype=object)
    y = y.astype(np.float32)

    return X, y

net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        ('input', layers.InputLayer),
        ('hidden', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(None,1196),
    hidden_num_units=100, # number of units in hidden layer
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=49,  # 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)