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Artificial Intelligence
What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`
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November 4, 2024
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Startup
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November 3, 2024
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October 1, 2024
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Big Data
Enhanced AI-Powered Real-Time Learning Platform
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September 19, 2024
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September 10, 2024
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This ‘sophisticated’ espresso machine transformed my morning espresso into a dream.
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September 9, 2024
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How did developers of the Indiana Jones video game manage to strike a balance between action-packed motion sequences and comedic moments?
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August 30, 2024
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Artificial Intelligence
Machine learning algorithms enable scientists to uncover the fundamental characteristics of animal species by analyzing vast amounts of data.
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August 30, 2024
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Cyber Security
The evolution of networking technologies has led to the development of innovative solutions such as Artificial Intelligence (AI), Machine Learning, and more, which have revolutionized the way networks operate and are managed.
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August 23, 2024
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![What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`](https://i2.wp.com/blogs.rstudio.com/tensorflow/posts/2018-07-30-attention-layer/images/attention.png?w=768&resize=768,0&ssl=1 "What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`")
