Python - Deep Learning with Lasagne

import numpy as np
import pandas as pd
np.random.seed(1337)  # for reproducibility
import lasagne
from lasagne import layers
from lasagne.layers import ReshapeLayer
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import visualize
import theano.tensor as T
import theano

df=pd.read_csv('DadosTeseLogit.csv',sep=',',header=0)
y=np.array(df[[30]])
y=[item for sublist in y for item in sublist]
x=np.array(df).T


x2=[]
for i in range (0,98):
    x2.append([x[18][i],x[19][i]])

target=y

target = np.array(y).astype(np.uint8)
train = np.array(x2).astype(np.float32)
test = np.array(x2).astype(np.float32)
target=target[20:98]
train=train[20:98]
test=test[0:19]

def build_mlp(input_var):
    l_in=lasagne.layers.InputLayer(shape=(None,2),input_var=input_var,W=theano.shared(np.random.normal(0, 0.01, (50, 100))))
    l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.5)
    l_hid1 = lasagne.layers.DenseLayer(l_in_drop, num_units=4,nonlinearity=lasagne.nonlinearities.sigmoid)
    l_in_drop2 = lasagne.layers.DropoutLayer(l_hid1, p=0.5)
    l_out = lasagne.layers.DenseLayer(l_in_drop2, num_units=2,nonlinearity=lasagne.nonlinearities.sigmoid)
    return l_out
    
input_var = T.fmatrix('inputs')
target_var = T.ivector('targets')

network = build_mlp(input_var)
prediction = lasagne.layers.get_output(network,deterministic=True)

loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()

params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.07, momentum=0.9)

test_prediction = lasagne.layers.get_output(network)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,target_var)
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),dtype=theano.config.floatX)

pred=T.eq(T.argmax(test_prediction, axis=1), target_var)

train_fn = theano.function([input_var, target_var], loss, updates=updates)
val_fn = theano.function([input_var, target_var], [test_acc])
predict = theano.function([input_var, target_var], prediction, updates=updates)

pred1=theano.function([input_var, target_var],pred)

pars = theano.function([input_var,target_var], params,updates=updates)

z=[]
for i in range(0,1000):
    z.append(train_fn(train,target))
    print('Epoch:',i,'| Loss=',z[-1])

b0=np.array(z[-1]).tolist()
b=np.array(val_fn(train,target)).tolist()

plt.plot(z[0:200],color='r',linewidth=2)
plt.title("LASAGNE TRAINING LOSS")
plt.show()
print('Train Accuracy:',1-np.sqrt(b0))
print('Test Accuracy:',float(np.array(b)))
print('              Weights below')
pars(train,target)
pred1(train,target)