print(type(Ytrain))
print(type(X[0]))
print(type(X[0][0]))
print(type(Ytrain[0]))
assert (len(X) == len(Ytrain))
nnet.fit(X, Ytrain)
from sklearn.model_selection import cross_val_predict
# ytrain_pred = cross_val_predict(nnet, X, Ytrain,cv=5)
print("Training done")
Ytrain_pred = nnet.predict(X)
correct = 0
for i in range(len(X)):
if (Ytrain_pred[i] == Ytrain[i]):
correct += 1
print(Ytrain_pred)
print("Training Accuracy={}%".format(correct / len(X) * 100))
tX = np.load("./test/test.npy")
testX = []
for a in tX:
# c = []
# for b in a:
# c.append(np.float32(b))
c = np.array(list(a))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
x = F.softmax(x, dim=-1)
return x
cnn = NeuralNetClassifier(
Cnn,
max_epochs=8,
lr=1,
optimizer=torch.optim.Adadelta,
# device='cuda', # uncomment this to train with CUDA
)
#train the module
cnn.fit(XCnn_train, y_train);
#Use validation set to see the accuracy
cnn_pred = cnn.predict(XCnn_test)
print(np.mean(cnn_pred == y_test))
#predict the test set
cnn_pred_test = cnn.predict(test)
# In[80]:
#write to .csv file
ID = np.arange(1,20001)
ID = ID.tolist()
data = zip(ID,cnn_pred_test)
with open('CNN_v6.csv', 'w',newline='') as outfile:
mywriter = csv.writer(outfile)
# manually add header