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keras (layers (Dense (output_shape), Conv2D/Conv1D/MaxPooling2D(1D)/Flatte…
keras
layers
Dense
output_shape
Conv2D/Conv1D/MaxPooling2D(1D)/Flatten
Embedding/AveragePooling2D/BatchNormalization
SimpleRNN/LSTM/GRU/Bidirectional
Add/concatenate/Dot
Activation/Dropout/RepeatVector
参数
optimizer
'rmsprop'/'adam'
optimizers.RMSprop/Adam
loss
'categorical_crossentropy'/'binary_crossentropy'/
'mse'/'mae'
losses.binary_crossentropy/
metrics
'accuracy'/'acc'/'mae'
metrics.binary_accuracy
activation
'relu'/'softmax'/'sigmoid'
kernel_regularizer
regularizers.l1/l2/l1_l2
input_shape
preprocessing
image
ImageDataGenerator
load_img
image_to_array/array_to_image
text
Tokenizer
fit_on_texts/word_index
texts_to_matrix/texts_to_sequences
text_to_word_sequence
sequence
pad_sequence
skipgrams
applications
Xception
InceptionV3
vgg16
callbacks
EarlyStopping
monitor
'val_acc'/'val_loss'
mode
'auto'/'max'/'min'
min_delta/patience/verbose
baseline/restore_best_weights
TensorBoard
log_dir='./logs', histogram_freq/batch_size
write_graph/write_grads/write_images
embeddings_freq/embeddings_layer_names
embeddings_metadata/embeddings_data
ModelCheckpoint
filepath/verbose/save_best_only/save_weights_only
mode
'auto'/'max'/'min'
monitor
'val_acc'/'val_loss'
models
Sequential()
add/compile/fit/evaluate/predict_classes/predict_proba
fit()->history
fit_generator/save/trainable_weights
save/load_weights/load_model
to_json/model_from_json;to_yaml/model_from_yaml
Model
Input
datasets
boston_housing/imdb/reuters/mnist
utils
np_utils.to_categorical