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tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +import json +import tensorflow as tf +import numpy as np +import requests +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences + +def solution_model(): + url = 'http://storage.googleapis.com/download.tensorflow.org/data/sarcasm.json' + #datastore = urllib.request.urlretrieve(url, 'sarcasm.json') + datastore = requests.get('http://storage.googleapis.com/download.tensorflow.org/data/sarcasm.json') + # DO NOT CHANGE THIS CODE OR THE TESTS MAY NOT WORK + vocab_size = 1000 + embedding_dim = 16 + max_length = 120 + trunc_type='post' + padding_type='post' + oov_tok = "" + training_size = 20000 + + sentences = [] + labels = [] + # YOUR CODE HERE + for item in datastore.json(): + sentences.append(item['headline']) + labels.append(item['is_sarcastic']) + + training_sentences = sentences[0:training_size] + testing_sentences = sentences[training_size:] + training_labels = labels[0:training_size] + testing_labels = labels[training_size:] + + tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok) + tokenizer.fit_on_texts(training_sentences) + + word_index = tokenizer.word_index + + training_sequences = tokenizer.texts_to_sequences(training_sentences) + training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type) + + testing_sequences = tokenizer.texts_to_sequences(testing_sentences) + testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type) + training_padded = np.array(training_padded) + training_labels = np.array(training_labels) + testing_padded = np.array(testing_padded) + testing_labels = np.array(testing_labels) + model = tf.keras.Sequential([ + tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length), + tf.keras.layers.GlobalAveragePooling1D(), + tf.keras.layers.Dense(24, activation='relu'), + # YOUR CODE HERE. KEEP THIS OUTPUT LAYER INTACT OR TESTS MAY FAIL + tf.keras.layers.Dense(1, activation='sigmoid') + ]) + num_epochs = 30 + model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) + model.fit(training_padded, training_labels, epochs=num_epochs, + validation_data=(testing_padded, testing_labels), verbose=2) + + reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) + + def decode_sentence(text): + return ' '.join([reverse_word_index.get(i, '?') for i in text]) + + #print(decode_sentence(training_padded[0])) + #print(training_sentences[2]) + #print(labels[2]) + e = model.layers[0] + weights = e.get_weights()[0] + #print(weights.shape) # shape: (vocab_size, embedding_dim) + return model + + +# Note that you'll need to save your model as a .h5 like this. +# When you press the Submit and Test button, your saved .h5 model will +# be sent to the testing infrastructure for scoring +# and the score will be returned to you. +if __name__ == '__main__': + vocab_size = 1000 + embedding_dim = 16 + max_length = 120 + trunc_type = 'post' + padding_type = 'post' + oov_tok = "" + training_size = 20000 + + model = solution_model() + #model.summary() + tokenizer = Tokenizer(oov_token="") + #sentence = "facebook reportedly working on healthcare features and apps" + #sentence = "sentence = I really think this is amazing. honest." + + + sentence = ["paul newman dies after consuming 51 hard-boiled eggs", + "chubby jewish boy dreams of one day being next apatow muse"] + tokenizer.fit_on_texts(sentence) + sequences = tokenizer.texts_to_sequences(sentence) + + + #sequence = tokenizer.texts_to_sequences([sentence]) + #print(sequence) + + + #sequences = tokenizer.texts_to_sequences(sentence) + padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type) + #print(padded) + print(model.predict(padded)) + #model.save("sarcasm.h5") \ No newline at end of file From d7191b01ac0127ebb8cbdee92a198d7be51036d5 Mon Sep 17 00:00:00 2001 From: Vasanth <87628135+vasanthri9q@users.noreply.github.com> Date: Tue, 6 Dec 2022 22:46:18 -0600 Subject: [PATCH 4/8] Update README.md --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a7b40d2cc8..7142ebc949 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ -# CourseProject +# CS410 Spiderman -Please fork this repository and paste the github link of your fork on Microsoft CMT. Detailed instructions are on Coursera under Week 1: Course Project Overview/Week 9 Activities. +Please click here for the video presentation: + Part I: https://youtu.be/0utoLsXVr9Y + Part II: https://youtu.be/o_V7v0G5xS8 From 8d6cb00470109d168190b7e9b40769594096a4ee Mon Sep 17 00:00:00 2001 From: Vasanth <87628135+vasanthri9q@users.noreply.github.com> Date: Tue, 6 Dec 2022 22:48:32 -0600 Subject: [PATCH 5/8] Update sarcasm_final.py --- sarcasm_final.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/sarcasm_final.py b/sarcasm_final.py index f118eabb27..a291642dda 100644 --- a/sarcasm_final.py +++ b/sarcasm_final.py @@ -4,12 +4,10 @@ import urllib from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences -import json import tensorflow as tf import numpy as np import requests -from tensorflow.keras.preprocessing.text import Tokenizer -from tensorflow.keras.preprocessing.sequence import pad_sequences + def solution_model(): url = 'http://storage.googleapis.com/download.tensorflow.org/data/sarcasm.json' @@ -57,7 +55,7 @@ def solution_model(): # YOUR CODE HERE. KEEP THIS OUTPUT LAYER INTACT OR TESTS MAY FAIL tf.keras.layers.Dense(1, activation='sigmoid') ]) - num_epochs = 30 + num_epochs = 10 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=2) @@ -96,8 +94,12 @@ def decode_sentence(text): #sentence = "sentence = I really think this is amazing. honest." - sentence = ["paul newman dies after consuming 51 hard-boiled eggs", - "chubby jewish boy dreams of one day being next apatow muse"] + sentence = ["obama visits arlington national cemetery to honor veterans", + "why writers must plan to be surprise", + "gillian jacobs on what it's like to kiss adam brody", + "rescuers heroically help beached garbage back into ocean", + "christian bale visits sikh temple victims", + "brita-unveils-new-in-throat-water-filters"] tokenizer.fit_on_texts(sentence) sequences = tokenizer.texts_to_sequences(sentence) @@ -110,4 +112,4 @@ def decode_sentence(text): padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type) #print(padded) print(model.predict(padded)) - #model.save("sarcasm.h5") \ No newline at end of file + #model.save("sarcasm.h5") From 8d072b60e969e66cc16e854a15bad779e02b0001 Mon Sep 17 00:00:00 2001 From: Vasanth <87628135+vasanthri9q@users.noreply.github.com> Date: Tue, 13 Dec 2022 21:45:03 -0600 Subject: [PATCH 6/8] Add files via upload --- CS410 - Final Project.docx | Bin 0 -> 17262 bytes Dockerise a Python Program.docx | Bin 0 -> 14681 bytes dockerfile | 27 +++++++++++++++++++++++++++ requirements.txt | 3 +++ 4 files changed, 30 insertions(+) create mode 100644 CS410 - Final Project.docx create mode 100644 Dockerise a Python Program.docx create mode 100644 dockerfile create mode 100644 requirements.txt diff --git a/CS410 - Final Project.docx b/CS410 - Final Project.docx new file mode 100644 index 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