Fix typos.
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README.md
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README.md
@ -164,9 +164,9 @@ model.fit(images, captions, batch_size=16, nb_epoch=100)
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```
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In the examples folder, you will find example models for real datasets:
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- CIFAR10 small images classification: convnet with realtime data augmentation
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- IMDB movie reviews: sentiment classification
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- Reuters newswires: topic classification
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- CIFAR10 small images classification: Convnet with realtime data augmentation
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- IMDB movie review sentiment classification: LSTM over sequences of words
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- Reuters newswires topic classification: Multilayer Perceptron
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## Warning
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@ -175,17 +175,17 @@ This is a 0.0.1 alpha release. Feature scope is limited, and wild bugs may appea
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## Current capabilities
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- model architectures:
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sequential (pipeline of layers)
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- Sequential (pipeline of layers)
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- layers:
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- layers.core:
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- Dense
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- Dropout
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- Activation
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- Embedding
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- Reshape
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- Flatten
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- Embedding
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- Repeat
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- RepeatVector
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- layers.convolutional:
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- Convolution2D
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- MaxPooling2D
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@ -259,7 +259,7 @@ sudo python setup.py install
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## Why this name, Keras?
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Keras (κέρας) means _horn_ in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the _Odyssee_, where dream spirits (_Oneiroi_, singular _Oneiros_) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).
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Keras (κέρας) means _horn_ in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the _Odyssey_, where dream spirits (_Oneiroi_, singular _Oneiros_) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).
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Keras was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
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@ -116,7 +116,7 @@ class RepeatVector(Layer):
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class Dense(Layer):
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'''
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Just your regular fully connecter NN layer.
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Just your regular fully connected NN layer.
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'''
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def __init__(self, input_dim, output_dim, init='uniform', activation='linear', weights=None):
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self.init = initializations.get(init)
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@ -141,7 +141,7 @@ class Dense(Layer):
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class Embedding(Layer):
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'''
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Turns a list of integers into a dense vector of fixed size.
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Turn a list of integers >=0 into a dense vector of fixed size.
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eg. [4, 50, 123, 26] -> [0.25, 0.1]
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@input_dim: size of vocabulary (highest input integer + 1)
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