From 13b5639d9c46aa424a0d0ad115492424f599e33c Mon Sep 17 00:00:00 2001 From: KonKon28 Date: Fri, 4 Oct 2024 17:56:52 +0200 Subject: [PATCH] ah yes Shakespeare --- main.ipynb | 597 ++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 541 insertions(+), 56 deletions(-) diff --git a/main.ipynb b/main.ipynb index 9308e12..33f6bee 100644 --- a/main.ipynb +++ b/main.ipynb @@ -28,21 +28,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": { "id": "BOwsuGQQY9OL", "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-16 22:09:18.737563: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], + "outputs": [], "source": [ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n", @@ -63,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "metadata": { "tags": [] }, @@ -89,11 +80,32 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['\\tSONNETS', 'TO THE ONLY BEGETTER OF', 'THESE INSUING SONNETS', 'MR. W. H. ALL HAPPINESS', 'AND THAT ETERNITY']\n" + ] + } + ], "source": [ - "# Your code here :" + "# Load the contents of the text file\n", + "file_path = './sonnets.txt'\n", + "\n", + "# Read the contents of the file into a variable (corpus)\n", + "with open(file_path, 'r') as file:\n", + " corpus = file.read().splitlines()\n", + "\n", + "# Remove any empty lines from the corpus\n", + "corpus = [line for line in corpus if line.strip()]\n", + "\n", + "# Display the first few lines to verify\n", + "print(corpus[:5])\n", + "\n", + "# Now you can proceed to use 'corpus' for further steps like tokenization or training.\n" ] }, { @@ -109,11 +121,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "# Your code here :" + "#Your code here:\n", + "# Step 1: Tokenize the corpus\n", + "tokenizer = Tokenizer(num_words=10000) # Adjust num_words if needed\n", + "tokenizer.fit_on_texts(corpus)\n", + "\n", + "# Step 2: Calculate total_words (vocabulary size)\n", + "# Ensure that +1 is added to account for the padding index\n", + "total_words = len(tokenizer.word_index) + 1" ] }, { @@ -130,11 +149,34 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-gram sequences: [[878], [3], [3, 2], [3, 2, 313], [3, 2, 313, 1375], [3, 2, 313, 1375, 4], [118], [118, 1376], [118, 1376, 878], [1377], [1377, 1378], [1377, 1378, 1379], [1377, 1378, 1379, 23], [1377, 1378, 1379, 23, 1380], [1], [1, 8], [1, 8, 517], [1381], [1381, 30], [126], [126, 186], [126, 186, 278], [126, 186, 278, 635], [126, 186, 278, 635, 1382], [2], [2, 98], [2, 98, 879], [1383], [1383, 7], [1384], [1384, 279], [880], [880, 880], [6], [34], [34, 418], [34, 418, 881], [34, 418, 881, 166], [34, 418, 881, 166, 214], [34, 418, 881, 166, 214, 518], [8], [8, 882], [8, 882, 135], [8, 882, 135, 353], [8, 882, 135, 353, 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[872, 138, 12, 513, 1], [872, 138, 12, 513, 1, 29], [872, 138, 12, 513, 1, 29, 30], [872, 138, 12, 513, 1, 29, 30, 8], [872, 138, 12, 513, 1, 29, 30, 8, 6], [872, 138, 12, 513, 1, 29, 30, 8, 6, 189], [102], [102, 253], [102, 253, 3373], [102, 253, 3373, 493], [102, 253, 3373, 493, 493], [102, 253, 3373, 493, 493, 3374], [102, 253, 3373, 493, 493, 3374, 14], [102, 253, 3373, 493, 493, 3374, 14, 15]]\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "# Step 1: Initialize an empty list to hold the n-gram sequences\n", + "input_sequences = []\n", + "\n", + "# Step 2: Loop through each sentence in the corpus\n", + "for line in corpus:\n", + " # Convert the sentence into a sequence of integers\n", + " token_list = tokenizer.texts_to_sequences([line])[0]\n", + " \n", + " # Step 3: Generate n-gram sequences for the sentence\n", + " for i in range(1, len(token_list) + 1):\n", + " n_gram_sequence = token_list[:i]\n", + " input_sequences.append(n_gram_sequence)\n", + "\n", + "# Output the generated n-gram sequences\n", + "print(\"N-gram sequences:\", input_sequences)\n" ] }, { @@ -149,11 +191,36 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max sequence length: 11\n", + "Shape of input_sequences: (17805, 11)\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "import numpy as np\n", + "\n", + "# Step 4: Find the maximum sequence length\n", + "max_sequence_len = max([len(x) for x in input_sequences])\n", + "\n", + "# Step 5: Pad the sequences\n", + "input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')\n", + "\n", + "# Step 6: Prepare Predictors and Labels\n", + "predictors = input_sequences[:, :-1]\n", + "labels = input_sequences[:, -1]\n", + "\n", + "# Output the max sequence length and the shape of input_sequences\n", + "print(\"Max sequence length:\", max_sequence_len)\n", + "print(\"Shape of input_sequences:\", input_sequences.shape)\n" ] }, { @@ -170,14 +237,32 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "metadata": { "id": "PRnDnCW-Z7qv", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of predictors: (17805, 10)\n", + "Shape of labels: (17805,)\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "# Step 1: Prepare Predictors (all elements except the last one)\n", + "predictors = input_sequences[:, :-1]\n", + "\n", + "# Step 2: Prepare Labels (the last element of each sequence)\n", + "labels = input_sequences[:, -1]\n", + "\n", + "# Output the shapes of predictors and labels\n", + "print(\"Shape of predictors:\", predictors.shape)\n", + "print(\"Shape of labels:\", labels.shape)\n" ] }, { @@ -197,11 +282,26 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of one-hot encoded labels: (17805, 3375)\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "from tensorflow.keras.utils import to_categorical\n", + "\n", + "# Step 1: One-hot encode the labels\n", + "labels = to_categorical(labels, num_classes=total_words)\n", + "\n", + "# Output the shape of the one-hot encoded labels\n", + "print(\"Shape of one-hot encoded labels:\", labels.shape)\n" ] }, { @@ -229,15 +329,38 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "model = Sequential([\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n", + "from tensorflow.keras.regularizers import l2\n", "\n", - " # Your code here :\n", - " \n", - "])" + "# Initialize the model\n", + "model = Sequential()\n", + "\n", + "# Add an Embedding layer\n", + "# Input dimension: total_words, output dimension: 100, input_length: max_sequence_len - 1\n", + "model.add(Embedding(input_dim=total_words, output_dim=100, input_length=max_sequence_len - 1))\n", + "\n", + "# Add a Bidirectional LSTM layer with 150 units, return_sequences=True\n", + "model.add(Bidirectional(LSTM(150, return_sequences=True)))\n", + "\n", + "# Add a Dropout layer with a dropout rate of 0.2\n", + "model.add(Dropout(0.2))\n", + "\n", + "# Add a second LSTM layer with 100 units\n", + "model.add(LSTM(100))\n", + "\n", + "# Add a Dense layer with half the total words (total_words // 2) and ReLU activation\n", + "# Regularization with L2 to prevent overfitting\n", + "model.add(Dense(total_words // 2, activation='relu', kernel_regularizer=l2(0.01)))\n", + "\n", + "# Add the final Dense layer with softmax activation to output probabilities\n", + "model.add(Dense(total_words, activation='softmax'))\n", + "\n", + "\n" ] }, { @@ -251,11 +374,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "# Your code here :" + "# Your code here :\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n" ] }, { @@ -269,11 +393,107 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 104ms/step - accuracy: 0.0326 - loss: 6.3021\n", + "Epoch 4/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 93ms/step - accuracy: 0.0439 - loss: 6.1817\n", + "Epoch 5/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 92ms/step - accuracy: 0.0461 - loss: 6.0688\n", + "Epoch 6/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 95ms/step - accuracy: 0.0503 - loss: 6.0132\n", + "Epoch 7/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 85ms/step - accuracy: 0.0516 - loss: 5.9340\n", + "Epoch 8/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 96ms/step - accuracy: 0.0537 - loss: 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loss: 4.8787\n", + "Epoch 20/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 92ms/step - accuracy: 0.1197 - loss: 4.7963\n", + "Epoch 21/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 92ms/step - accuracy: 0.1208 - loss: 4.7391\n", + "Epoch 22/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 93ms/step - accuracy: 0.1283 - loss: 4.6505\n", + "Epoch 23/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 93ms/step - accuracy: 0.1371 - loss: 4.5333\n", + "Epoch 24/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 93ms/step - accuracy: 0.1557 - loss: 4.4493\n", + "Epoch 25/50\n", + "\u001b[1m557/557\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 86ms/step - accuracy: 0.4295 - loss: 3.0207\n", + "Epoch 48/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 87ms/step - accuracy: 0.4514 - loss: 2.9487\n", + "Epoch 49/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 87ms/step - accuracy: 0.4585 - loss: 2.9187\n", + "Epoch 50/50\n", + "\u001b[1m557/557\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 87ms/step - accuracy: 0.4644 - loss: 2.8565\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# Train the model for 50 epochs\n", + "history = model.fit(predictors, labels, epochs=50, verbose=1)\n", + "\n", + "\n" ] }, { @@ -319,14 +652,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": { "id": "1fXTEO3GJ282", "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "# Plot the training loss\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(history.history['loss'])\n", + "plt.title('Training Loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.show()" ] }, { @@ -345,11 +696,51 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Write the text you want the model to use as a starting point to generate the next words ' eye on thee good hate ' canst you not say thence you dearer age blind stand days still due on me thence thine art me on you back on truth well ' art to go rage spent dote rare spent spent prove thee appear life new prove thee '\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "import numpy as np\n", + "\n", + "# Set the seed text and the number of words to generate\n", + "seed_text = 'Write the text you want the model to use as a starting point to generate the next words'\n", + "next_words = 50 # Change this to the number of words you want to generate\n", + "\n", + "# Function to generate text\n", + "for _ in range(next_words):\n", + " # Tokenize the seed text\n", + " token_list = tokenizer.texts_to_sequences([seed_text])[0]\n", + " \n", + " # Pad the sequence to match the max sequence length\n", + " token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1, padding='pre')\n", + " \n", + " # Predict the next word\n", + " predicted = model.predict(token_list, verbose=0)\n", + " \n", + " # Get the index of the predicted word\n", + " predicted_word_index = np.argmax(predicted, axis=-1)\n", + " \n", + " # Get the word from the index\n", + " output_word = \"\"\n", + " for word, index in tokenizer.word_index.items():\n", + " if index == predicted_word_index:\n", + " output_word = word\n", + " break\n", + " \n", + " # Append the word to the seed text\n", + " seed_text += \" \" + output_word\n", + "\n", + "# Output the generated text\n", + "print(seed_text)\n" ] }, { @@ -365,14 +756,45 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": { "id": "6Vc6PHgxa6Hm", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated Text:\n", + " Once upon a time there was a several plot pride tell ' set ' grew of state away on men can set thee back on to thee one night ' eye that life ' blind art for thee say good night old night away in thee well young on true night well days still still still prove\n" + ] + } + ], "source": [ - "# Your code here :" + "# Set the seed text and the number of words to generate\n", + "seed_text = \"Once upon a time there was a\"\n", + "next_words = 50 # Specify how many words to generate\n", + "\n", + "# Loop to generate the text\n", + "for _ in range(next_words):\n", + " # Tokenize and pad the seed text\n", + " token_list = tokenizer.texts_to_sequences([seed_text])[0]\n", + " token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1, padding='pre')\n", + " \n", + " # Predict the next word index\n", + " predicted = model.predict(token_list, verbose=0)\n", + " predicted_word_index = np.argmax(predicted, axis=-1)[0]\n", + " \n", + " # Find the corresponding word\n", + " output_word = [word for word, index in tokenizer.word_index.items() if index == predicted_word_index]\n", + " \n", + " # Add the new word to the seed text\n", + " if output_word:\n", + " seed_text += ' ' + output_word[0]\n", + "\n", + "# Print the final generated text\n", + "print(\"Generated Text:\\n\", seed_text)\n" ] }, { @@ -384,11 +806,74 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Seed: Once upon a time, there was Donald Trump\n", + "Generated Text:\n", + "Once upon a time, there was Donald Trump they despise twain best spent ride 'will ' dote me do ' ' do me new art so life best ' go age ' stand ' do me well prove thee thee still still due ' prove thee bring mine eye be prove thee life still days ' ' say\n", + "\n", + "\n", + "Seed: The sun is shining brightly\n", + "Generated Text:\n", + "The sun is shining brightly dove it pass things strong dream are night to latch eyes back new say so art love new ' hast you still due on me old due ' bring forth thee new die so art not must go night on kings ' ' ' bear many rage ' canst see\n", + "\n", + "\n", + "Seed: In the middle of the night\n", + "Generated Text:\n", + "In the middle of the night hath rehearse rehearse rehearse ' tell the spring and say it ' do sing on the ground hour night it bow ' dote go age ' stand can prove old minds to ' do go age ' art on give forth to me bow back so life bring many night\n", + "\n" + ] + } + ], "source": [ - "# Your code here :" + "# Your code here :\n", + "import numpy as np\n", + "\n", + "# Step 1: Define three seed texts\n", + "seeds = [\n", + " \"Once upon a time, there was Donald Trump\",\n", + " \"The sun is shining brightly\", \n", + " \"In the middle of the night\", \n", + "]\n", + "\n", + "# Number of words to generate for each seed\n", + "next_words = 50\n", + "\n", + "# Step 2: Loop through each seed text and generate text\n", + "for seed_text in seeds:\n", + " print(f\"\\nSeed: {seed_text}\")\n", + " current_seed_text = seed_text # Make a copy of the seed text for modifications\n", + "\n", + " # Generate the next set of words\n", + " for _ in range(next_words):\n", + " # Tokenize the seed text\n", + " token_list = tokenizer.texts_to_sequences([current_seed_text])[0]\n", + " \n", + " # Pad the sequence to the max_sequence_len - 1\n", + " token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1, padding='pre')\n", + " \n", + " # Predict the next word\n", + " predicted = model.predict(token_list, verbose=0)\n", + " predicted_word_index = np.argmax(predicted, axis=-1)[0]\n", + " \n", + " # Convert the predicted index to the corresponding word\n", + " output_word = \"\"\n", + " for word, index in tokenizer.word_index.items():\n", + " if index == predicted_word_index:\n", + " output_word = word\n", + " break\n", + " \n", + " # Append the predicted word to the seed text\n", + " current_seed_text += \" \" + output_word\n", + "\n", + " # Print the generated text\n", + " print(f\"Generated Text:\\n{current_seed_text}\\n\")\n" ] } ], @@ -400,7 +885,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -414,7 +899,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" } }, "nbformat": 4,