keras topN显示,自编写代码案例_Python

对于使用已经训练好的模型，比如VGG,RESNET等，keras都自带了一个keras.applications.imagenet_utils.decode_predictions的方法,有很多限制：

									def decode_predictions(preds, top=5):

									 """Decodes the prediction of an ImageNet model.

									 # Arguments

									 preds: Numpy tensor encoding a batch of predictions.

									 top: Integer, how many top-guesses to return.

									 # Returns

									 A list of lists of top class prediction tuples

									 `(class_name, class_description, score)`.

									 One list of tuples per sample in batch input.

									 # Raises

									 ValueError: In case of invalid shape of the `pred` array

									  (must be 2D).

									 """

									 global CLASS_INDEX

									 if len(preds.shape) != 2 or preds.shape[1] != 1000:

									 raise ValueError('`decode_predictions` expects '

									    'a batch of predictions '

									    '(i.e. a 2D array of shape (samples, 1000)). '

									    'Found array with shape: ' + str(preds.shape))

									 if CLASS_INDEX is None:

									 fpath = get_file('imagenet_class_index.json',

									    CLASS_INDEX_PATH,

									    cache_subdir='models',

									    file_hash='c2c37ea517e94d9795004a39431a14cb')

									 with open(fpath) as f:

									  CLASS_INDEX = json.load(f)

									 results = []

									 for pred in preds:

									 top_indices = pred.argsort()[-top:][::-1]

									 result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices]

									 result.sort(key=lambda x: x[2], reverse=True)

									 results.append(result)

									 return results

把重要的东西挖出来，然后自己敲,这样就OK了，下例以MNIST数据集为例：

									import keras

									from keras.models import Sequential

									from keras.layers import Dense

									import numpy as np

									import tflearn

									import tflearn.datasets.mnist as mnist

									def decode_predictions_custom(preds, top=5):

									 CLASS_CUSTOM = ["0","1","2","3","4","5","6","7","8","9"]

									 results = []

									 for pred in preds:

									 top_indices = pred.argsort()[-top:][::-1]

									 result = [tuple(CLASS_CUSTOM[i]) + (pred[i]*100,) for i in top_indices]

									 results.append(result)

									 return results

									x_train, y_train, x_test, y_test = mnist.load_data(one_hot=True)

									model = Sequential()

									model.add(Dense(units=64, activation='relu', input_dim=784))

									model.add(Dense(units=10, activation='softmax'))

									model.compile(loss='categorical_crossentropy',

									  optimizer='sgd',

									  metrics=['accuracy'])

									model.fit(x_train, y_train, epochs=10, batch_size=128)

									# score = model.evaluate(x_test, y_test, batch_size=128)

									# print(score)

									preds = model.predict(x_test[0:1,:])

									p = decode_predictions_custom(preds)

									for (i,(label,prob)) in enumerate(p[0]):

									 print("{}. {}: {:.2f}%".format(i+1, label,prob)) 

									# 1. 7: 99.43%

									# 2. 9: 0.24%

									# 3. 3: 0.23%

									# 4. 0: 0.05%

									# 5. 2: 0.03%

补充知识：keras简单的去噪自编码器代码和各种类型自编码器代码

我就废话不多说了，大家还是直接看代码吧~

									start = time()

									from keras.models import Sequential

									from keras.layers import Dense, Dropout,Input

									from keras.layers import Embedding

									from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D

									from keras import layers

									from keras.models import Model

									# Parameters for denoising autoencoder

									nb_visible = 120

									nb_hidden = 64

									batch_size = 16

									# Build autoencoder model

									input_img = Input(shape=(nb_visible,))

									encoded = Dense(nb_hidden, activation='relu')(input_img)

									decoded = Dense(nb_visible, activation='sigmoid')(encoded)

									autoencoder = Model(input=input_img, output=decoded)

									autoencoder.compile(loss='mean_squared_error',optimizer='adam',metrics=['mae'])

									autoencoder.summary()

									# Train

									### 加一个early_stooping

									import keras 

									early_stopping = keras.callbacks.EarlyStopping(

									  monitor='val_loss',

									  min_delta=0.0001,

									  patience=5, 

									  verbose=0, 

									  mode='auto'

									)

									autoencoder.fit(X_train_np, y_train_np, nb_epoch=50, batch_size=batch_size , shuffle=True,

									        callbacks = [early_stopping],verbose = 1,validation_data=(X_test_np, y_test_np))

									# Evaluate

									evaluation = autoencoder.evaluate(X_test_np, y_test_np, batch_size=batch_size , verbose=1)

									print('val_loss: %.6f, val_mean_absolute_error: %.6f' % (evaluation[0], evaluation[1]))

									end = time()

									print('耗时：'+str((end-start)/60))