I am running a simple CNN for a classification task. There is something is wrong with the way I am reading the array data as a tensor. I am running into this error AttributeError: 'numpy.ndarray' object has no attribute 'dim
. Any suggestions?
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Net(nn.Module):
# define nn
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, 3)
self.softmax = nn.Softmax(dim=1)
def forward(self, X):
X = F.relu(self.fc1(X))
X = self.fc2(X)
X = self.fc3(X)
X = self.softmax(X)
return X
# load IRIS dataset
dataset = pd.read_csv('dataset/iris.csv')
# transform species to numerics
dataset.loc[dataset.species=='Iris-setosa', 'species'] = 0
dataset.loc[dataset.species=='Iris-versicolor', 'species'] = 1
dataset.loc[dataset.species=='Iris-virginica', 'species'] = 2
train_X, test_X, train_y, test_y = train_test_split(dataset[dataset.columns[0:4]].values,
dataset.species.values, test_size=0.8)
# wrap up with Variable in pytorch
#train_X = Variable(torch.Tensor(train_X).float())
#test_X = Variable(torch.Tensor(test_X).float())
#train_y = Variable(torch.Tensor(train_y).long())
#test_y = Variable(torch.Tensor(test_y).long())
train_X = np.float32(train_X)
test_X = np.float32(test_X)
train_y = np.int64(train_y)
test_y = np.int64(test_y)
net = Net()
criterion = nn.CrossEntropyLoss()# cross entropy loss
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
for epoch in range(1000):
optimizer.zero_grad()
out = net(train_X)
loss = criterion(out, train_y)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print('number of epoch', epoch, 'loss', loss.data[0])
predict_out = net(test_X)
_, predict_y = torch.max(predict_out, 1)
print('prediction accuracy', accuracy_score(test_y.data, predict_y.data))
print('macro precision', precision_score(test_y.data, predict_y.data, average='macro'))
print('micro precision', precision_score(test_y.data, predict_y.data, average='micro'))
print('macro recall', recall_score(test_y.data, predict_y.data, average='macro'))
print('micro recall', recall_score(test_y.data, predict_y.data, average='micro'))