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        "author": "CyberZHG",
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        "description": "# Keras Transformer\n\n[![Travis](https://travis-ci.org/CyberZHG/keras-transformer.svg)](https://travis-ci.org/CyberZHG/keras-transformer)\n[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-transformer/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-transformer)\n[![Version](https://img.shields.io/pypi/v/keras-transformer.svg)](https://pypi.org/project/keras-transformer/)\n![Downloads](https://img.shields.io/pypi/dm/keras-transformer.svg)\n![License](https://img.shields.io/pypi/l/keras-transformer.svg)\n\n![](https://img.shields.io/badge/keras-tensorflow-blue.svg)\n![](https://img.shields.io/badge/keras-theano-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/eager-blue.svg)\n![](https://img.shields.io/badge/keras-tf.keras/2.0_beta-blue.svg)\n\n \\[[\u4e2d\u6587](https://github.com/CyberZHG/keras-transformer/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-transformer/blob/master/README.md)\\]\n\nImplementation of [transformer](https://arxiv.org/pdf/1706.03762.pdf) for seq2seq tasks.\n\n## Install\n\n```bash\npip install keras-transformer\n```\n\n## Usage\n\n### Train\n\n```python\nimport numpy as np\nfrom keras_transformer import get_model\n\n# Build a small toy token dictionary\ntokens = 'all work and no play makes jack a dull boy'.split(' ')\ntoken_dict = {\n    '<PAD>': 0,\n    '<START>': 1,\n    '<END>': 2,\n}\nfor token in tokens:\n    if token not in token_dict:\n        token_dict[token] = len(token_dict)\n\n# Generate toy data\nencoder_inputs_no_padding = []\nencoder_inputs, decoder_inputs, decoder_outputs = [], [], []\nfor i in range(1, len(tokens) - 1):\n    encode_tokens, decode_tokens = tokens[:i], tokens[i:]\n    encode_tokens = ['<START>'] + encode_tokens + ['<END>'] + ['<PAD>'] * (len(tokens) - len(encode_tokens))\n    output_tokens = decode_tokens + ['<END>', '<PAD>'] + ['<PAD>'] * (len(tokens) - len(decode_tokens))\n    decode_tokens = ['<START>'] + decode_tokens + ['<END>'] + ['<PAD>'] * (len(tokens) - len(decode_tokens))\n    encode_tokens = list(map(lambda x: token_dict[x], encode_tokens))\n    decode_tokens = list(map(lambda x: token_dict[x], decode_tokens))\n    output_tokens = list(map(lambda x: [token_dict[x]], output_tokens))\n    encoder_inputs_no_padding.append(encode_tokens[:i + 2])\n    encoder_inputs.append(encode_tokens)\n    decoder_inputs.append(decode_tokens)\n    decoder_outputs.append(output_tokens)\n\n# Build the model\nmodel = get_model(\n    token_num=len(token_dict),\n    embed_dim=30,\n    encoder_num=3,\n    decoder_num=2,\n    head_num=3,\n    hidden_dim=120,\n    attention_activation='relu',\n    feed_forward_activation='relu',\n    dropout_rate=0.05,\n    embed_weights=np.random.random((13, 30)),\n)\nmodel.compile(\n    optimizer='adam',\n    loss='sparse_categorical_crossentropy',\n)\nmodel.summary()\n\n# Train the model\nmodel.fit(\n    x=[np.asarray(encoder_inputs * 1000), np.asarray(decoder_inputs * 1000)],\n    y=np.asarray(decoder_outputs * 1000),\n    epochs=5,\n)\n```\n\n### Predict\n\n```python\nfrom keras_transformer import decode\n\ndecoded = decode(\n    model,\n    encoder_inputs_no_padding,\n    start_token=token_dict['<START>'],\n    end_token=token_dict['<END>'],\n    pad_token=token_dict['<PAD>'],\n    max_len=100,\n)\ntoken_dict_rev = {v: k for k, v in token_dict.items()}\nfor i in range(len(decoded)):\n    print(' '.join(map(lambda x: token_dict_rev[x], decoded[i][1:-1])))\n```\n\n### Translation\n\n```python\nimport numpy as np\nfrom keras_transformer import get_model, decode\n\nsource_tokens = [\n    'i need more power'.split(' '),\n    'eat jujube and pill'.split(' '),\n]\ntarget_tokens = [\n    list('\u6211\u8981\u66f4\u591a\u7684\u629b\u74e6'),\n    list('\u5403\u67a3\ud83d\udc8a'),\n]\n\n# Generate dictionaries\ndef build_token_dict(token_list):\n    token_dict = {\n        '<PAD>': 0,\n        '<START>': 1,\n        '<END>': 2,\n    }\n    for tokens in token_list:\n        for token in tokens:\n            if token not in token_dict:\n                token_dict[token] = len(token_dict)\n    return token_dict\n\nsource_token_dict = build_token_dict(source_tokens)\ntarget_token_dict = build_token_dict(target_tokens)\ntarget_token_dict_inv = {v: k for k, v in target_token_dict.items()}\n\n# Add special tokens\nencode_tokens = [['<START>'] + tokens + ['<END>'] for tokens in source_tokens]\ndecode_tokens = [['<START>'] + tokens + ['<END>'] for tokens in target_tokens]\noutput_tokens = [tokens + ['<END>', '<PAD>'] for tokens in target_tokens]\n\n# Padding\nsource_max_len = max(map(len, encode_tokens))\ntarget_max_len = max(map(len, decode_tokens))\n\nencode_tokens = [tokens + ['<PAD>'] * (source_max_len - len(tokens)) for tokens in encode_tokens]\ndecode_tokens = [tokens + ['<PAD>'] * (target_max_len - len(tokens)) for tokens in decode_tokens]\noutput_tokens = [tokens + ['<PAD>'] * (target_max_len - len(tokens)) for tokens in output_tokens]\n\nencode_input = [list(map(lambda x: source_token_dict[x], tokens)) for tokens in encode_tokens]\ndecode_input = [list(map(lambda x: target_token_dict[x], tokens)) for tokens in decode_tokens]\ndecode_output = [list(map(lambda x: [target_token_dict[x]], tokens)) for tokens in output_tokens]\n\n# Build & fit model\nmodel = get_model(\n    token_num=max(len(source_token_dict), len(target_token_dict)),\n    embed_dim=32,\n    encoder_num=2,\n    decoder_num=2,\n    head_num=4,\n    hidden_dim=128,\n    dropout_rate=0.05,\n    use_same_embed=False,  # Use different embeddings for different languages\n)\nmodel.compile('adam', 'sparse_categorical_crossentropy')\nmodel.summary()\n\nmodel.fit(\n    x=[np.array(encode_input * 1024), np.array(decode_input * 1024)],\n    y=np.array(decode_output * 1024),\n    epochs=10,\n    batch_size=32,\n)\n\n# Predict\ndecoded = decode(\n    model,\n    encode_input,\n    start_token=target_token_dict['<START>'],\n    end_token=target_token_dict['<END>'],\n    pad_token=target_token_dict['<PAD>'],\n)\nprint(''.join(map(lambda x: target_token_dict_inv[x], decoded[0][1:-1])))\nprint(''.join(map(lambda x: target_token_dict_inv[x], decoded[1][1:-1])))\n```\n\n### Beam Search\n\nIn `decode`, the word with top probability is selected as the predicted token by default. You can enable beam search by setting `top_k` and `temperature`:\n\n```python\ndecoded = decode(\n    model,\n    encode_input,\n    start_token=target_token_dict['<START>'],\n    end_token=target_token_dict['<END>'],\n    pad_token=target_token_dict['<PAD>'],\n    top_k=10,\n    temperature=1.0,\n)\nprint(''.join(map(lambda x: target_token_dict_inv[x], decoded[0][1:-1])))\nprint(''.join(map(lambda x: target_token_dict_inv[x], decoded[1][1:-1])))\n```",
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