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{
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-27T01:27:47.394726Z",
"iopub.status.busy": "2020-09-27T01:27:47.394164Z",
"iopub.status.idle": "2020-09-27T01:27:47.395964Z",
"shell.execute_reply": "2020-09-27T01:27:47.396326Z"
},
"id": "PED3bIpOYkBu"
},
"outputs": [],
"source": [
"class Transformer(tf.keras.Model):\n",
" def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, \n",
" target_vocab_size, pe_input, pe_target, rate=0.1):\n",
" super(Transformer, self).__init__()\n",
"\n",
" self.encoder = Encoder(num_layers, d_model, num_heads, dff, \n",
" input_vocab_size, pe_input, rate)\n",
"\n",
" self.decoder = Decoder(num_layers, d_model, num_heads, dff, \n",
" target_vocab_size, pe_target, rate)\n",
"\n",
" self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n",
" \n",
" def call(self, inp, tar, training, enc_padding_mask, \n",
" look_ahead_mask, dec_padding_mask):\n",
"\n",
" enc_output = self.encoder(inp, training, enc_padding_mask) # (batch_size, inp_seq_len, d_model)\n",
" \n",
" '''\n",
" The output of the last layer of the encoder is passed to all the layers of the decoder. \n",
" '''\n",
" #dec_output.shape == (batch_size, tar_seq_len, d_model)\n",
" dec_output, attention_weights = self.decoder(\n",
" tar, enc_output, training, look_ahead_mask, dec_padding_mask)\n",
" \n",
" '''\n",
" Tee final part of Transformer model. In case of machine translation, you predict a \n",
" 'target_vocab_size' dimensional vector at every potition of the target sentence. \n",
" '''\n",
" final_output = self.final_layer(dec_output) # (batch_size, tar_seq_len, target_vocab_size)\n",
" \n",
" return final_output, attention_weights"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"num_layers = 4\n",
"d_model = 128\n",
"dff = 512\n",
"num_heads = 8\n",
"\n",
"input_vocab_size = 10000 + 2\n",
"target_vocab_size = 10000 + 2\n",
"dropout_rate = 0.1"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"sample_transformer = Transformer(num_layers, d_model, num_heads, dff,\n",
" input_vocab_size, target_vocab_size, \n",
" pe_input=input_vocab_size, \n",
" pe_target=target_vocab_size,\n",
" rate=dropout_rate)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(64, 37, 10002)\n"
]
}
],
"source": [
"# Let's put in sample inputs and targets in the sample Transformer model. \n",
"# In this case, the max length of the input sentences is 38, and that of targets is 37. \n",
"# In practice, all the elements of 'sample_input' and 'sample_target' are integers. \n",
"sample_input = tf.random.uniform((64, 38), dtype=tf.int64, minval=0, maxval=200)\n",
"sample_target = tf.random.uniform((64, 37), dtype=tf.int64, minval=0, maxval=200)\n",
"\n",
"fn_out, _ = sample_transformer(sample_input, sample_target, training=False, \n",
" enc_padding_mask=None, \n",
" look_ahead_mask=None,\n",
" dec_padding_mask=None)\n",
"\n",
"print(fn_out.shape) # (batch_size, tar_seq_len, target_vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# As you can see, each target entences is a (37, 10002) sized matrix. "
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [
"s_qNSzzyaCbD"
],
"name": "transformer.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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