103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
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import os
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import pandas as pd
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import tensorflow as tf
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import numpy as np
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import torch
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import torch.nn as nn
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from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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print('debug 1')
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train_df = pd.read_csv('train/in.tsv', header=None, sep='\t')
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test_df = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
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dev_df = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
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train_expected = pd.read_csv('train/expected.tsv', header=None, sep='\t')
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train_text = train_df[0].tolist()
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test_text = test_df[0].tolist()
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dev_text = test_df[0].tolist()
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text_data = train_text + test_text + dev_text
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vectorize_layer = TextVectorization(max_tokens=5, output_mode="int")
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text_data = tf.data.Dataset.from_tensor_slices(text_data)
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vectorize_layer.adapt(text_data.batch(64))
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inputs = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="text")
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outputs = vectorize_layer(inputs)
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model = tf.keras.Model(inputs, outputs)
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print('uwaga debug')
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x_train = list(map(model.predict, train_text))
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y_train = train_expected[0]
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x_test = list(map(model.predict, test_text))
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loss_function = nn.CrossEntropyLoss()
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x_train = pd.DataFrame(x_train)
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x_test = pd.DataFrame(x_test)
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y_train = pd.DataFrame(y_train[0])
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# (model.predict(["Murder in the forset!"]))
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class FeedforwardNeuralNetModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(FeedforwardNeuralNetModel, self).__init__()
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# Linear function
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self.fc1 = nn.Linear(input_dim, hidden_dim)
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# Non-linearity
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self.sigmoid = nn.Sigmoid()
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# Linear function (readout)
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self.fc2 = nn.Linear(hidden_dim, output_dim)
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def forward(self, x):
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# Linear function # LINEAR
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out = self.fc1(x)
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# Non-linearity # NON-LINEAR
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out = self.sigmoid(out)
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# Linear function (readout) # LINEAR
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out = self.fc2(out)
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return out
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num_epochs = 2
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for epoch in range(num_epochs):
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if (epoch + 1) % 25 == 0:
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print("Epoch completed: " + str(epoch + 1))
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print(f"Epoch number: {epoch}")
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train_loss = 0
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for index, row in x_train.iterrows():
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print(index)
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# Forward pass to get output
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probs = x_train[0][index]
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# Get the target label
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target = y_train[0][index]
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# Calculate Loss: softmax --> cross entropy loss
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loss = loss_function(probs, target)
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# Accumulating the loss over time
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train_loss += loss.item()
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# Getting gradients w.r.t. parameters
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loss.backward()
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train_loss = 0
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bow_ff_nn_predictions = []
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original_lables_ff_bow = []
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with torch.no_grad():
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for index, row in x_test.iterrows():
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probs = x_test[0][index]
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bow_ff_nn_predictions.append(torch.argmax(probs, dim=1).cpu().numpy()[0])
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print(bow_ff_nn_predictions)
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