for i in range(25):
img = X_train[y_train == 7][i].reshape(28, 28)
ax[i].imshow(img, cmap='Greys')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
# Save data in compressed npz file
#np.savez_compressed('mnist_scaled.npz', X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
mnist = np.load('mnist_scaled.npz')
mnist.files
X_test, X_train, y_train, y_test = [mnist[f] for f in mnist.files]
from neuralnet import NeuralNetMLP
nn = NeuralNetMLP(n_hidden=100,
l2=0.01,
epochs=200,
eta=0.0005,
minibatch_size=100,
shuffle=True,
seed=1)
nn.fit(X_train=X_train[:55000],
y_train=y_train[:55000],
X_valid=X_train[55000:],
y_valid=y_train[55000:])
# ax[0].set_xticks([])
# ax[0].set_yticks([])
# plt.tight_layout()
# plt.show()
#
# np.savetxt('train_img.csv', X_train,fmt='%i', delimiter='.')
# np.savetxt('train_labels.csv', y_train,fmt='%i', delimiter='.')
# np.savetxt('test_img.csv', X_test,fmt='%i', delimiter='.')
# np.savetxt('test_labels.csv', y_test,fmt='%i', delimiter='.')
nn = NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=50,
l2=0.1,
l1=0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
shuffle=True,
minibatches=50,
random_state=1)
nn.fit(X_train, y_train, print_progress=True)
plt.plot(range(len(nn.cost_)), nn_cost_)
plt.ylim([0, 2000])
plt.ylabel('Cost')
plt.xlabel('Epochs * 50')
plt.tight_layout()
plt.show()
import time
import numpy as np
from neuralnet import NeuralNetMLP
X_train = np.genfromtxt('train_img.csv' , dtype=int, delimiter=',')
y_train = np.genfromtxt('train_labels.csv', dtype=int, delimiter=',')
X_test = np.genfromtxt('test_img.csv', dtype=int, delimiter=',')
y_test = np.genfromtxt('test_labels.csv', dtype=int, delimiter=',')
nn = NeuralNetMLP(n_output=10,
n_features= X_train.shape[1],
n_hidden = 50,
l2 = 0.1,
l1 = 0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
shuffle=True,
minibatches=50,
random_state= 1)
start_time = time.time()
nn.fit(X_train,y_train, print_progress=True)
y_train_pred = nn.predict(X_train)
y_test_pred = nn.predict(X_test)
end_time = time.time()
labels = np.fromfile(lbpath, dtype=np.uint8)
with open(images_path, 'rb') as imgpath:
magic, num, rows, cols = struct.unpack(">IIII", imgpath.read(16))
images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)
return images, labels
X_train, y_train = load_mnist('mnist', kind='train')
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))
from neuralnet import NeuralNetMLP
# new neural network with 10 hidden layers, no regularization (l1, l2) = (0, 0)
# no adaotuve learning, no momentum learning and regular gradient descent
# using minibatches to 1
nn_check = NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=10,
l2=0.0,
l1=0.0,
epochs=10,
eta=0.001,
alpha=0.0,
decrease_const=0.0,
minibatches=1,
random_state=1)
nn_check.fit(X_train[:5], y_train[:5], print_progress=False)
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
plt.show()
### 003 implement a multi-layer perceptron
print('### 003')
from neuralnet import NeuralNetMLP
nn = NeuralNetMLP(n_output=10,
n_features=X_train.shape[1],
n_hidden=50,
l2=0.1,
l1=0.0,
epochs=1000,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
shuffle=True,
minibatches=50,
random_state=1)
nn.fit(X_train, y_train, print_progress=True)
# only plot every 50th step to account for the 50 mini-batches (50 mini-batches x 1000 epochs)
plt.plot(range(len(nn.cost_)), nn.cost_)
plt.ylim([0, 2000])
plt.ylabel('Cost')
plt.xlabel('Epochs * 50')
plt.tight_layout()
plt.show()
X_trn = [YOUR CODE HERE]
y_trn = [YOUR CODE HERE]
X_vld = [YOUR CODE HERE]
y_vld = [YOUR CODE HERE]
X_tst = [YOUR CODE HERE]
y_tst = [YOUR CODE HERE]
# =====================================================================
# 多層パーセプトロン(MLP)のインスタンスの生成と学習
nn = NeuralNetMLP(n_output=10, # 出力ユニット数
n_features=X_trn.shape[1], # 入力ユニット数
n_hidden=30, # 隠れユニット数
l2=0.1, # L2正則化のλパラメータ
l1=0.0, # L1正則化のλパラメータ
epochs=600, # 学習エポック数
eta=0.001, # 学習率の初期値
alpha = 0.001, # モーメンタム学習の1つ前の勾配の係数
decrease_const=0.00001, # 適応学習率の減少定数
minibatches=50, # 各エポックでのミニバッチ数
shuffle=True, # データのシャッフル
random_state=3) # 乱数シードの状態
nn.fit(X_trn, y_trn, print_progress=True)
plt.figure(0)
plt.plot(range(len(nn.cost_)), nn.cost_)
plt.ylim([0, 1000])
plt.ylabel('Cost')
plt.xlabel('Epochs * 50')
plt.tight_layout()
img = img.reshape(28, 28)
ax[i].imshow(img, cmap='Greys')
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
fig.savefig('mnist_0_9.pdf')
y_train=np.argmax(y_train, axis=1)
y_test=np.argmax(y_test, axis=1)
np.savez_compressed('mnist_scaled.npz', X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
'''
mnist = np.load('mnist_scaled.npz')
X_train, X_test, y_train, y_test = mnist['X_train'], mnist['X_test'], mnist['y_train'], mnist['y_test']
from neuralnet import NeuralNetMLP
nn = NeuralNetMLP(n_hidden=100, l2=0.01, epochs=200, eta=0.005, minibatch_size=100, shuffle=True, seed=1)
nn.fit(X_train=X_train[:50000], y_train=y_train[:50000], X_valid=X_train[50000:], y_valid=y_train[50000:])
fig = plt.figure()
plt.plot(range(nn.epochs), nn.eval_['cost'])
plt.ylabel('Cost')
plt.xlabel('Epochs')
fig.savefig('NeuralNetMLP_cost.pdf')
fig = plt.figure()
plt.plot(range(nn.epochs), nn.eval_['train_acc'], label='training')
plt.plot(range(nn.epochs), nn.eval_['valid_acc'], label='validation', linestyle='--')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
fig.savefig('NeuralNetMLP_acc.pdf')
from neuralnet import NeuralNetMLP
from mnist_data import load_mnist
import numpy as np
import matplotlib.pyplot as plt
from wine import X_train, X_test, y_train, y_test
nn = NeuralNetMLP(n_output=3,
n_features=X_train.shape[1],
n_hidden=50,
l2=0.0,
l1=0.0,
epochs=40,
eta=0.001,
alpha=0.001,
decrease_const=0.00001,
minibatches=20,
shuffle=True,
random_state=1)
nn.fit(X_train, y_train, print_progress=True)
import sys
# Predict on training Data
y_train_pred = nn.predict(X_train)
if sys.version_info < (3, 0):
acc = (np.sum(y_train == y_train_pred,
axis=0)).astype('float') / X_train.shape[0]
else:
acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
