From ea69c1a3bb214d23f96eb77443366dd20554e7a0 Mon Sep 17 00:00:00 2001 From: Francois Date: Sat, 28 Mar 2015 01:46:10 -0700 Subject: [PATCH] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 696757f37..576b97acd 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ model.add(Dense(20, 64, init='uniform', activation='tanh')) model.add(Dropout(0.5)) model.add(Dense(64, 64, init='uniform', activation='tanh')) model.add(Dropout(0.5)) -model.add(Dense(64, 1, init='uniform', activation='sigmoid') +model.add(Dense(64, 1, init='uniform', activation='softmax') sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='mean_squared_error', optimizer=sgd) @@ -106,7 +106,7 @@ from keras.layers.recurrent import LSTM model = Sequential() model.add(Embedding(max_features, 256)) -model.add(LSTM(256, 128), activation='sigmoid', inner_activation='hard_sigmoid') +model.add(LSTM(256, 128, activation='sigmoid', inner_activation='hard_sigmoid')) model.add(Dropout(0.5)) model.add(Dense(128, 1)) model.add(Activation('sigmoid')) @@ -152,7 +152,7 @@ model.add(Dropout(0.5)) model.add(Repeat(max_caption_len)) # the GRU below returns sequences of max_caption_len vectors of size 256 (our word embedding size) -model.add(GRU(256, 256), return_sequences=True) +model.add(GRU(256, 256, return_sequences=True)) model.compile(loss='mean_squared_error', optimizer='rmsprop') @@ -164,9 +164,9 @@ model.fit(images, captions, batch_size=16, nb_epoch=100) ``` In the examples folder, you will find example models for real datasets: - - CIFAR10 small images classification: convnet with realtime data augmentation - - IMDB movie reviews: sentiment classification - - Reuters newswires: topic classification +- CIFAR10 small images classification: convnet with realtime data augmentation +- IMDB movie reviews: sentiment classification +- Reuters newswires: topic classification ## Warning