def fit(datasets_name, batch_size, max_epoch, learning_rate, momentum_rate, weight_decay, lambda_1, curves):
from deepnet import MLP
# from deepnet import ConvNet
import gzip
import pickle
import numpy as np
logger.info(datasets_name)
if datasets_name == 'Mnist':
f = gzip.open('../../datasets/Mnist/mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = pickle.load(f,encoding='bytes')
f.close()
X_train, y_train = train_set
X_valid, y_valid = valid_set
X_test, y_test = test_set
# X_train = np.reshape(X_train, (X_train.shape[0], 1, 28, 28))
# X_valid = np.reshape(X_valid, (X_valid.shape[0], 1, 28, 28))
# X_test = np.reshape(X_test, (X_test.shape[0], 1, 28, 28))
y_test = np.array(y_test)
mlp = MLP(X_train, y_train, X_valid, y_valid, batch_size)
mlp.fit(max_epoch, learning_rate, momentum_rate, weight_decay, lambda_1, curves)
from deepnet import MLP
import gzip
import pickle
f = gzip.open('../../datasets/Mnist/mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = pickle.load(f,encoding='bytes')
f.close()
X_train, y_train = train_set
X_valid, y_valid = valid_set
mlp = MLP(X_train, y_train, X_valid, y_valid, 200)
mlp.fit(5, 0.1, 0.9, 0, 0, True)