def test_ae(x_train_, y_train_, x_test_, y_test_):
"""
AE测试
"""
rst = []
# 数据展平
x_train_ = x_train_.reshape(x_train_.shape[0], -1)
x_test_ = x_test_.reshape(x_test_.shape[0], -1)
# 测试数据一半用于svc训练，一半用于测试
x_test_train = x_test_[:5000]
y_test_train = y_test_[:5000]
x_test_test = x_test_[5000:]
y_test_test = y_test_[5000:]
# k对应不同降维目标
for k in range(10, 200, 10):
# 训练pca
ae = AutoEncoder(28 * 28, k, 28 * 28)
ae.fit(x_train_, x_train_)
# 训练svc分类器
svc = SVC(gamma='scale')
svc.fit(ae.encode(x_test_train), y_test_train)
# 测试分类器
y_pred = svc.predict(ae.encode(x_test_test))
accuracy = accuracy_score(y_test_test, y_pred)
print(accuracy)
rst.append(accuracy)
return rst
print("XShape: ", X.shape)
print("Making Predictions")
X_pred = (ae.predict(X_train)* sigma + mu).reshape(-1, 28, 28).astype(int).clip(0,255).astype('uint8')
print("Pred Shape: ", X_pred.shape)
get_random_images()
IPImage('images/orig.png')
print("Encoding Training Data")
X_encoded=ae.encode(X_train)
print("Encoded Shape: ",X_encoded.shape)
print("Decoding Data")
X_decoded = (ae.decode(X_encoded) * sigma + mu).reshape(-1, 28, 28).astype(int).clip(0,255).astype('uint8')
print("Decoded Shape: ",X_decoded.shape)
# check it worked:
pic_array = get_picture_array(X_decoded, np.random.randint(len(X_decoded)))
image = Image.fromarray(pic_array)
image.save('images/test.png', format="PNG")
IPImage('images/test.png')
print("Done.")
X_train = X_train.astype(np.float32)
X_out = X_train.reshape((X_train.shape[0], -1))
print("XShape: ", X.shape)
print("Making Predictions")
X_pred = (ae.predict(X_train) * sigma + mu).reshape(-1, 28,
28).astype(int).clip(
0, 255).astype('uint8')
print("Pred Shape: ", X_pred.shape)
get_random_images()
IPImage('images/orig.png')
print("Encoding Training Data")
X_encoded = ae.encode(X_train)
print("Encoded Shape: ", X_encoded.shape)
print("Decoding Data")
X_decoded = (ae.decode(X_encoded) * sigma + mu).reshape(
-1, 28, 28).astype(int).clip(0, 255).astype('uint8')
print("Decoded Shape: ", X_decoded.shape)
# check it worked:
pic_array = get_picture_array(X_decoded, np.random.randint(len(X_decoded)))
image = Image.fromarray(pic_array)
image.save('images/test.png', format="PNG")
IPImage('images/test.png')
print("Done.")
def _compress(self, df, index=''):
ae = AutoEncoder(df.shape[0], int(df.shape[0] / 2))
ae.train(df.values, 2, 20, verbose=True)
return pd.DataFrame(ae.encode(df.values.ix[:, df.columns != index]),
index=index)
