From 90a1b7ff6bf5f6b67684600880272674097cf297 Mon Sep 17 00:00:00 2001 From: Sachin Prasad Date: Mon, 13 May 2024 11:26:39 -0700 Subject: [PATCH] Migrate Review classification using Active learning to Keras 3. (#1857) * Migarte to Keras 3 * Migrate to Keras 3 * trim extra epoch outputs --- .../active_learning_review_classification.py | 34 +- ...learning_review_classification_15_1755.png | Bin 0 -> 29261 bytes ...learning_review_classification_15_1756.png | Bin 0 -> 26518 bytes ...learning_review_classification_15_2073.png | Bin 0 -> 30250 bytes ...learning_review_classification_15_2074.png | Bin 0 -> 27048 bytes ...learning_review_classification_17_2582.png | Bin 0 -> 32667 bytes ...learning_review_classification_17_2583.png | Bin 0 -> 33596 bytes ...learning_review_classification_17_2767.png | Bin 0 -> 33324 bytes ...learning_review_classification_17_2768.png | Bin 0 -> 34443 bytes ...ctive_learning_review_classification.ipynb | 59 +- .../active_learning_review_classification.md | 1270 ++++++++++++++--- 11 files changed, 1080 insertions(+), 283 deletions(-) create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_15_1755.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_15_1756.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_15_2073.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_15_2074.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_17_2582.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_17_2583.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_17_2767.png create mode 100644 examples/nlp/img/active_learning_review_classification/active_learning_review_classification_17_2768.png diff --git a/examples/nlp/active_learning_review_classification.py b/examples/nlp/active_learning_review_classification.py index 6c86f67276..d6ec81780e 100644 --- a/examples/nlp/active_learning_review_classification.py +++ b/examples/nlp/active_learning_review_classification.py @@ -2,9 +2,10 @@ Title: Review Classification using Active Learning Author: [Darshan Deshpande](https://twitter.com/getdarshan) Date created: 2021/10/29 -Last modified: 2021/10/29 +Last modified: 2024/05/08 Description: Demonstrating the advantages of active learning through review classification. Accelerator: GPU +Converted to Keras 3 by: [Sachin Prasad](https://github.com/sachinprasadhs) """ """ @@ -51,10 +52,14 @@ ## Importing required libraries """ +import os + +os.environ["KERAS_BACKEND"] = "tensorflow" # @param ["tensorflow", "jax", "torch"] +import keras +from keras import ops +from keras import layers import tensorflow_datasets as tfds import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers import matplotlib.pyplot as plt import re import string @@ -169,16 +174,8 @@ """ -def custom_standardization(input_data): - lowercase = tf.strings.lower(input_data) - stripped_html = tf.strings.regex_replace(lowercase, "
", " ") - return tf.strings.regex_replace( - stripped_html, f"[{re.escape(string.punctuation)}]", "" - ) - - vectorizer = layers.TextVectorization( - 3000, standardize=custom_standardization, output_sequence_length=150 + 3000, standardize="lower_and_strip_punctuation", output_sequence_length=150 ) # Adapting the dataset vectorizer.adapt( @@ -289,7 +286,7 @@ def train_full_model(full_train_dataset, val_dataset, test_dataset): callbacks=[ keras.callbacks.EarlyStopping(patience=4, verbose=1), keras.callbacks.ModelCheckpoint( - "FullModelCheckpoint.h5", verbose=1, save_best_only=True + "FullModelCheckpoint.keras", verbose=1, save_best_only=True ), ], ) @@ -303,7 +300,7 @@ def train_full_model(full_train_dataset, val_dataset, test_dataset): ) # Loading the best checkpoint - model = keras.models.load_model("FullModelCheckpoint.h5") + model = keras.models.load_model("FullModelCheckpoint.keras") print("-" * 100) print( @@ -370,6 +367,7 @@ def train_active_learning_models( num_iterations=3, sampling_size=5000, ): + # Creating lists for storing metrics losses, val_losses, accuracies, val_accuracies = [], [], [], [] @@ -389,7 +387,7 @@ def train_active_learning_models( # Defining checkpoints. # The checkpoint callback is reused throughout the training since it only saves the best overall model. checkpoint = keras.callbacks.ModelCheckpoint( - "AL_Model.h5", save_best_only=True, verbose=1 + "AL_Model.keras", save_best_only=True, verbose=1 ) # Here, patience is set to 4. This can be set higher if desired. early_stopping = keras.callbacks.EarlyStopping(patience=4, verbose=1) @@ -413,9 +411,9 @@ def train_active_learning_models( predictions = model.predict(test_dataset) # Generating labels from the output probabilities - rounded = tf.where(tf.greater(predictions, 0.5), 1, 0) + rounded = ops.where(ops.greater(predictions, 0.5), 1, 0) - # Evaluating the number of zeros and ones incorrectly classified + # Evaluating the number of zeros and ones incorrrectly classified _, _, false_negatives, false_positives = model.evaluate(test_dataset, verbose=0) print("-" * 100) @@ -482,7 +480,7 @@ def train_active_learning_models( ) # Loading the best model from this training loop - model = keras.models.load_model("AL_Model.h5") + model = keras.models.load_model("AL_Model.keras") # Plotting the overall history and evaluating the final model plot_history(losses, val_losses, accuracies, val_accuracies) diff --git 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--git a/examples/nlp/ipynb/active_learning_review_classification.ipynb b/examples/nlp/ipynb/active_learning_review_classification.ipynb index b3fc0a1010..5b25393f48 100644 --- a/examples/nlp/ipynb/active_learning_review_classification.ipynb +++ b/examples/nlp/ipynb/active_learning_review_classification.ipynb @@ -10,7 +10,7 @@ "\n", "**Author:** [Darshan Deshpande](https://twitter.com/getdarshan)
\n", "**Date created:** 2021/10/29
\n", - "**Last modified:** 2021/10/29
\n", + "**Last modified:** 2024/05/08
\n", "**Description:** Demonstrating the advantages of active learning through review classification." ] }, @@ -33,7 +33,7 @@ "ensure consistency in labeling of new data.\n", "\n", "The process starts with annotating a small subset of the full dataset and training an\n", - "initial model. The best model checkpoint is saved and then tested on a balanced test\n", + "initial model. The best model checkpoint is saved and then tested on\u00a0a balanced test\n", "set. The test set must be carefully sampled because the full training process will be\n", "dependent on it. Once we have the initial evaluation scores, the oracle is tasked with\n", "labeling more samples; the number of data points to be sampled is usually determined by\n", @@ -70,16 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\" # @param [\"tensorflow\", \"jax\", \"torch\"]\n", + "import keras\n", + "from keras import ops\n", + "from keras import layers\n", "import tensorflow_datasets as tfds\n", "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", "import matplotlib.pyplot as plt\n", "import re\n", "import string\n", @@ -102,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, @@ -133,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, @@ -229,23 +233,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ - "\n", - "def custom_standardization(input_data):\n", - " lowercase = tf.strings.lower(input_data)\n", - " stripped_html = tf.strings.regex_replace(lowercase, \"
\", \" \")\n", - " return tf.strings.regex_replace(\n", - " stripped_html, f\"[{re.escape(string.punctuation)}]\", \"\"\n", - " )\n", - "\n", "\n", "vectorizer = layers.TextVectorization(\n", - " 3000, standardize=custom_standardization, output_sequence_length=150\n", + " 3000, standardize=\"lower_and_strip_punctuation\", output_sequence_length=150\n", ")\n", "# Adapting the dataset\n", "vectorizer.adapt(\n", @@ -283,12 +279,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ + "\n", "# Helper function for merging new history objects with older ones\n", "def append_history(losses, val_losses, accuracy, val_accuracy, history):\n", " losses = losses + history.history[\"loss\"]\n", @@ -312,7 +309,8 @@ " plt.legend([\"train_accuracy\", \"val_accuracy\"])\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(\"Accuracy\")\n", - " plt.show()\n" + " plt.show()\n", + "" ] }, { @@ -331,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, @@ -351,7 +349,8 @@ " ]\n", " )\n", " model.summary()\n", - " return model\n" + " return model\n", + "" ] }, { @@ -368,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, @@ -395,7 +394,7 @@ " callbacks=[\n", " keras.callbacks.EarlyStopping(patience=4, verbose=1),\n", " keras.callbacks.ModelCheckpoint(\n", - " \"FullModelCheckpoint.h5\", verbose=1, save_best_only=True\n", + " \"FullModelCheckpoint.keras\", verbose=1, save_best_only=True\n", " ),\n", " ],\n", " )\n", @@ -409,7 +408,7 @@ " )\n", "\n", " # Loading the best checkpoint\n", - " model = keras.models.load_model(\"FullModelCheckpoint.h5\")\n", + " model = keras.models.load_model(\"FullModelCheckpoint.keras\")\n", "\n", " print(\"-\" * 100)\n", " print(\n", @@ -474,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 0, "metadata": { "colab_type": "code" }, @@ -510,7 +509,7 @@ " # Defining checkpoints.\n", " # The checkpoint callback is reused throughout the training since it only saves the best overall model.\n", " checkpoint = keras.callbacks.ModelCheckpoint(\n", - " \"AL_Model.h5\", save_best_only=True, verbose=1\n", + " \"AL_Model.keras\", save_best_only=True, verbose=1\n", " )\n", " # Here, patience is set to 4. This can be set higher if desired.\n", " early_stopping = keras.callbacks.EarlyStopping(patience=4, verbose=1)\n", @@ -534,9 +533,9 @@ " predictions = model.predict(test_dataset)\n", "\n", " # Generating labels from the output probabilities\n", - " rounded = tf.where(tf.greater(predictions, 0.5), 1, 0)\n", + " rounded = ops.where(ops.greater(predictions, 0.5), 1, 0)\n", "\n", - " # Evaluating the number of zeros and ones classified\n", + " # Evaluating the number of zeros and ones incorrrectly classified\n", " _, _, false_negatives, false_positives = model.evaluate(test_dataset, verbose=0)\n", "\n", " print(\"-\" * 100)\n", @@ -603,7 +602,7 @@ " )\n", "\n", " # Loading the best model from this training loop\n", - " model = keras.models.load_model(\"AL_Model.h5\")\n", + " model = keras.models.load_model(\"AL_Model.keras\")\n", "\n", " # Plotting the overall history and evaluating the final model\n", " plot_history(losses, val_losses, accuracies, val_accuracies)\n", @@ -682,4 +681,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/examples/nlp/md/active_learning_review_classification.md b/examples/nlp/md/active_learning_review_classification.md index 7f6de09bdf..a0c15f1adb 100644 --- a/examples/nlp/md/active_learning_review_classification.md +++ b/examples/nlp/md/active_learning_review_classification.md @@ -2,13 +2,13 @@ **Author:** [Darshan Deshpande](https://twitter.com/getdarshan)
**Date created:** 2021/10/29
-**Last modified:** 2021/10/29
+**Last modified:** 2024/05/08
+**Description:** Demonstrating the advantages of active learning through review classification. [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/active_learning_review_classification.ipynb) • [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlp/active_learning_review_classification.py) -**Description:** Demonstrating the advantages of active learning through review classification. --- ## Introduction @@ -54,10 +54,14 @@ Selects data points closest to the decision boundary ```python +import os + +os.environ["KERAS_BACKEND"] = "tensorflow" # @param ["tensorflow", "jax", "torch"] +import keras +from keras import ops +from keras import layers import tensorflow_datasets as tfds import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers import matplotlib.pyplot as plt import re import string @@ -192,16 +196,8 @@ faster, we use the `map()` function with its parallelization functionality. ```python -def custom_standardization(input_data): - lowercase = tf.strings.lower(input_data) - stripped_html = tf.strings.regex_replace(lowercase, "
", " ") - return tf.strings.regex_replace( - stripped_html, f"[{re.escape(string.punctuation)}]", "" - ) - - vectorizer = layers.TextVectorization( - 3000, standardize=custom_standardization, output_sequence_length=150 + 3000, standardize="lower_and_strip_punctuation", output_sequence_length=150 ) # Adapting the dataset vectorizer.adapt( @@ -233,6 +229,7 @@ test_dataset = test_dataset.batch(256).map( ```python + # Helper function for merging new history objects with older ones def append_history(losses, val_losses, accuracy, val_accuracy, history): losses = losses + history.history["loss"] @@ -317,7 +314,7 @@ def train_full_model(full_train_dataset, val_dataset, test_dataset): callbacks=[ keras.callbacks.EarlyStopping(patience=4, verbose=1), keras.callbacks.ModelCheckpoint( - "FullModelCheckpoint.h5", verbose=1, save_best_only=True + "FullModelCheckpoint.keras", verbose=1, save_best_only=True ), ], ) @@ -331,7 +328,7 @@ def train_full_model(full_train_dataset, val_dataset, test_dataset): ) # Loading the best checkpoint - model = keras.models.load_model("FullModelCheckpoint.h5") + model = keras.models.load_model("FullModelCheckpoint.keras") print("-" * 100) print( @@ -354,90 +351,291 @@ full_train_dataset = ( full_dataset_model = train_full_model(full_train_dataset, val_dataset, test_dataset) ``` + +

Model: "sequential"
+
+ + + + +
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+│ embedding (Embedding)           │ (None, 150, 128)       │       384,000 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ bidirectional (Bidirectional)   │ (None, 150, 64)        │        41,216 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ global_max_pooling1d            │ (None, 64)             │             0 │
+│ (GlobalMaxPooling1D)            │                        │               │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dense (Dense)                   │ (None, 20)             │         1,300 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dropout (Dropout)               │ (None, 20)             │             0 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dense_1 (Dense)                 │ (None, 1)              │            21 │
+└─────────────────────────────────┴────────────────────────┴───────────────┘
+
+ + + + +
 Total params: 426,537 (1.63 MB)
+
+ + + + +
 Trainable params: 426,537 (1.63 MB)
+
+ + + + +
 Non-trainable params: 0 (0.00 B)
+
+ + +
``` -Model: "sequential" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - embedding (Embedding) (None, 150, 128) 384000 - - bidirectional (Bidirectiona (None, 150, 64) 41216 - l) - - global_max_pooling1d (Globa (None, 64) 0 - lMaxPooling1D) - - dense (Dense) (None, 20) 1300 - - dropout (Dropout) (None, 20) 0 - - dense_1 (Dense) (None, 1) 21 - -================================================================= -Total params: 426,537 -Trainable params: 426,537 -Non-trainable params: 0 -_________________________________________________________________ Epoch 1/20 -156/157 [============================>.] - ETA: 0s - loss: 0.5150 - binary_accuracy: 0.7615 - false_negatives: 3314.0000 - false_positives: 6210.0000 -Epoch 00001: val_loss improved from inf to 0.47791, saving model to FullModelCheckpoint.h5 -157/157 [==============================] - 25s 103ms/step - loss: 0.5148 - binary_accuracy: 0.7617 - false_negatives: 3316.0000 - false_positives: 6217.0000 - val_loss: 0.4779 - val_binary_accuracy: 0.7858 - val_false_negatives: 970.0000 - val_false_positives: 101.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 73ms/step - binary_accuracy: 0.6412 - false_negatives: 2084.3333 - false_positives: 5252.1924 - loss: 0.6507 + + +
+``` +Epoch 1: val_loss improved from inf to 0.57198, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 15s 79ms/step - binary_accuracy: 0.6411 - false_negatives: 2135.1772 - false_positives: 5292.4053 - loss: 0.6506 - val_binary_accuracy: 0.7356 - val_false_negatives: 898.0000 - val_false_positives: 424.0000 - val_loss: 0.5720 + + +
+``` Epoch 2/20 -156/157 [============================>.] - ETA: 0s - loss: 0.3659 - binary_accuracy: 0.8500 - false_negatives: 2833.0000 - false_positives: 3158.0000 -Epoch 00002: val_loss improved from 0.47791 to 0.35345, saving model to FullModelCheckpoint.h5 -157/157 [==============================] - 9s 59ms/step - loss: 0.3656 - binary_accuracy: 0.8501 - false_negatives: 2836.0000 - false_positives: 3159.0000 - val_loss: 0.3535 - val_binary_accuracy: 0.8502 - val_false_negatives: 363.0000 - val_false_positives: 386.0000 + +``` +
+ 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.7448 - false_negatives: 1756.2756 - false_positives: 3249.1411 - loss: 0.5416 + + +
+``` +Epoch 2: val_loss improved from 0.57198 to 0.41756, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.7450 - false_negatives: 1783.8925 - false_positives: 3279.8101 - loss: 0.5412 - val_binary_accuracy: 0.8156 - val_false_negatives: 531.0000 - val_false_positives: 391.0000 - val_loss: 0.4176 + + +
+``` Epoch 3/20 -156/157 [============================>.] - ETA: 0s - loss: 0.3319 - binary_accuracy: 0.8653 - false_negatives: 2507.0000 - false_positives: 2873.0000 -Epoch 00003: val_loss improved from 0.35345 to 0.33150, saving model to FullModelCheckpoint.h5 -157/157 [==============================] - 9s 55ms/step - loss: 0.3319 - binary_accuracy: 0.8652 - false_negatives: 2512.0000 - false_positives: 2878.0000 - val_loss: 0.3315 - val_binary_accuracy: 0.8576 - val_false_negatives: 423.0000 - val_false_positives: 289.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8162 - false_negatives: 1539.7693 - false_positives: 2197.1475 - loss: 0.4254 + + +
+``` +Epoch 3: val_loss improved from 0.41756 to 0.38233, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8161 - false_negatives: 1562.6962 - false_positives: 2221.5886 - loss: 0.4254 - val_binary_accuracy: 0.8340 - val_false_negatives: 496.0000 - val_false_positives: 334.0000 - val_loss: 0.3823 + + +
+``` Epoch 4/20 -156/157 [============================>.] - ETA: 0s - loss: 0.3130 - binary_accuracy: 0.8764 - false_negatives: 2398.0000 - false_positives: 2538.0000 -Epoch 00004: val_loss did not improve from 0.33150 -157/157 [==============================] - 9s 55ms/step - loss: 0.3129 - binary_accuracy: 0.8763 - false_negatives: 2404.0000 - false_positives: 2542.0000 - val_loss: 0.3328 - val_binary_accuracy: 0.8586 - val_false_negatives: 263.0000 - val_false_positives: 444.0000 + +``` +
+ 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8413 - false_negatives: 1400.6538 - false_positives: 1818.7372 - loss: 0.3837 + + +
+``` +Epoch 4: val_loss improved from 0.38233 to 0.36235, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8412 - false_negatives: 1421.5063 - false_positives: 1839.3102 - loss: 0.3838 - val_binary_accuracy: 0.8396 - val_false_negatives: 548.0000 - val_false_positives: 254.0000 - val_loss: 0.3623 + + +
+``` Epoch 5/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2918 - binary_accuracy: 0.8867 - false_negatives: 2141.0000 - false_positives: 2385.0000 -Epoch 00005: val_loss did not improve from 0.33150 -157/157 [==============================] - 9s 55ms/step - loss: 0.2917 - binary_accuracy: 0.8867 - false_negatives: 2143.0000 - false_positives: 2388.0000 - val_loss: 0.3762 - val_binary_accuracy: 0.8468 - val_false_negatives: 476.0000 - val_false_positives: 290.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8611 - false_negatives: 1264.5256 - false_positives: 1573.5962 - loss: 0.3468 + + +
+``` +Epoch 5: val_loss did not improve from 0.36235 + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 75ms/step - binary_accuracy: 0.8611 - false_negatives: 1283.0632 - false_positives: 1592.3228 - loss: 0.3468 - val_binary_accuracy: 0.8222 - val_false_negatives: 734.0000 - val_false_positives: 155.0000 - val_loss: 0.4081 + + +
+``` Epoch 6/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2819 - binary_accuracy: 0.8901 - false_negatives: 2112.0000 - false_positives: 2277.0000 -Epoch 00006: val_loss did not improve from 0.33150 -157/157 [==============================] - 9s 55ms/step - loss: 0.2819 - binary_accuracy: 0.8902 - false_negatives: 2112.0000 - false_positives: 2282.0000 - val_loss: 0.4018 - val_binary_accuracy: 0.8312 - val_false_negatives: 694.0000 - val_false_positives: 150.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8706 - false_negatives: 1186.9166 - false_positives: 1427.9487 - loss: 0.3301 + + +
+``` +Epoch 6: val_loss improved from 0.36235 to 0.35041, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8705 - false_negatives: 1204.8038 - false_positives: 1444.9368 - loss: 0.3302 - val_binary_accuracy: 0.8412 - val_false_negatives: 569.0000 - val_false_positives: 225.0000 - val_loss: 0.3504 + + +
+``` Epoch 7/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2650 - binary_accuracy: 0.8992 - false_negatives: 1902.0000 - false_positives: 2122.0000 -Epoch 00007: val_loss improved from 0.33150 to 0.32843, saving model to FullModelCheckpoint.h5 -157/157 [==============================] - 9s 55ms/step - loss: 0.2649 - binary_accuracy: 0.8992 - false_negatives: 1908.0000 - false_positives: 2123.0000 - val_loss: 0.3284 - val_binary_accuracy: 0.8578 - val_false_negatives: 274.0000 - val_false_positives: 437.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8768 - false_negatives: 1162.4423 - false_positives: 1342.4807 - loss: 0.3084 + + +
+``` +Epoch 7: val_loss improved from 0.35041 to 0.32680, saving model to FullModelCheckpoint.keras + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8768 - false_negatives: 1179.5253 - false_positives: 1358.4114 - loss: 0.3085 - val_binary_accuracy: 0.8590 - val_false_negatives: 364.0000 - val_false_positives: 341.0000 - val_loss: 0.3268 + + +
+``` Epoch 8/20 -157/157 [==============================] - ETA: 0s - loss: 0.2508 - binary_accuracy: 0.9051 - false_negatives: 1821.0000 - false_positives: 1974.0000 -Epoch 00008: val_loss did not improve from 0.32843 -157/157 [==============================] - 9s 55ms/step - loss: 0.2508 - binary_accuracy: 0.9051 - false_negatives: 1821.0000 - false_positives: 1974.0000 - val_loss: 0.4806 - val_binary_accuracy: 0.8194 - val_false_negatives: 788.0000 - val_false_positives: 115.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 73ms/step - binary_accuracy: 0.8865 - false_negatives: 1079.3206 - false_positives: 1250.2693 - loss: 0.2924 + + +
+``` +Epoch 8: val_loss did not improve from 0.32680 + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8864 - false_negatives: 1094.9873 - false_positives: 1265.0632 - loss: 0.2926 - val_binary_accuracy: 0.8460 - val_false_negatives: 548.0000 - val_false_positives: 222.0000 - val_loss: 0.3432 + + +
+``` Epoch 9/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2377 - binary_accuracy: 0.9112 - false_negatives: 1771.0000 - false_positives: 1775.0000 -Epoch 00009: val_loss did not improve from 0.32843 -157/157 [==============================] - 9s 54ms/step - loss: 0.2378 - binary_accuracy: 0.9112 - false_negatives: 1775.0000 - false_positives: 1777.0000 - val_loss: 0.3378 - val_binary_accuracy: 0.8562 - val_false_negatives: 335.0000 - val_false_positives: 384.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 73ms/step - binary_accuracy: 0.8912 - false_negatives: 1019.1987 - false_positives: 1189.4551 - loss: 0.2807 + + +
+``` +Epoch 9: val_loss did not improve from 0.32680 + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 77ms/step - binary_accuracy: 0.8912 - false_negatives: 1033.9684 - false_positives: 1203.5632 - loss: 0.2808 - val_binary_accuracy: 0.8588 - val_false_negatives: 330.0000 - val_false_positives: 376.0000 - val_loss: 0.3302 + + +
+``` Epoch 10/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2209 - binary_accuracy: 0.9195 - false_negatives: 1591.0000 - false_positives: 1623.0000 -Epoch 00010: val_loss did not improve from 0.32843 -157/157 [==============================] - 9s 55ms/step - loss: 0.2211 - binary_accuracy: 0.9195 - false_negatives: 1594.0000 - false_positives: 1627.0000 - val_loss: 0.3475 - val_binary_accuracy: 0.8556 - val_false_negatives: 425.0000 - val_false_positives: 297.0000 + +``` +
+ + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8997 - false_negatives: 968.6346 - false_positives: 1109.9103 - loss: 0.2669 + + +
+``` +Epoch 10: val_loss did not improve from 0.32680 + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.8996 - false_negatives: 983.1202 - false_positives: 1123.3418 - loss: 0.2671 - val_binary_accuracy: 0.8558 - val_false_negatives: 445.0000 - val_false_positives: 276.0000 - val_loss: 0.3413 + + +
+``` Epoch 11/20 -156/157 [============================>.] - ETA: 0s - loss: 0.2060 - binary_accuracy: 0.9251 - false_negatives: 1512.0000 - false_positives: 1479.0000 -Epoch 00011: val_loss did not improve from 0.32843 -157/157 [==============================] - 9s 55ms/step - loss: 0.2061 - binary_accuracy: 0.9251 - false_negatives: 1517.0000 - false_positives: 1479.0000 - val_loss: 0.3823 - val_binary_accuracy: 0.8522 - val_false_negatives: 276.0000 - val_false_positives: 463.0000 -Epoch 00011: early stopping ```
-![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_15_1.png) + + 156/157 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9055 - false_negatives: 937.0320 - false_positives: 1000.8589 - loss: 0.2520 + + +
+``` +Epoch 11: val_loss did not improve from 0.32680 + + +``` +
+ 157/157 ━━━━━━━━━━━━━━━━━━━━ 12s 76ms/step - binary_accuracy: 0.9055 - false_negatives: 950.3608 - false_positives: 1013.6456 - loss: 0.2521 - val_binary_accuracy: 0.8602 - val_false_negatives: 402.0000 - val_false_positives: 297.0000 - val_loss: 0.3281 + + +
+``` +Epoch 11: early stopping + +``` +
+ +![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_15_1755.png) + -![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_15_2.png) + +![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_15_1756.png) +
``` ---------------------------------------------------------------------------------------------------- -Test set evaluation: {'loss': 0.34183189272880554, 'binary_accuracy': 0.8579999804496765, 'false_negatives': 295.0, 'false_positives': 415.0} + +Test set evaluation: {'binary_accuracy': 0.8507999777793884, 'false_negatives': 397.0, 'false_positives': 349.0, 'loss': 0.3372706174850464} ---------------------------------------------------------------------------------------------------- ``` @@ -508,7 +706,7 @@ def train_active_learning_models( # Defining checkpoints. # The checkpoint callback is reused throughout the training since it only saves the best overall model. checkpoint = keras.callbacks.ModelCheckpoint( - "AL_Model.h5", save_best_only=True, verbose=1 + "AL_Model.keras", save_best_only=True, verbose=1 ) # Here, patience is set to 4. This can be set higher if desired. early_stopping = keras.callbacks.EarlyStopping(patience=4, verbose=1) @@ -532,9 +730,9 @@ def train_active_learning_models( predictions = model.predict(test_dataset) # Generating labels from the output probabilities - rounded = tf.where(tf.greater(predictions, 0.5), 1, 0) + rounded = ops.where(ops.greater(predictions, 0.5), 1, 0) - # Evaluating the number of zeros and ones incorrectly classified + # Evaluating the number of zeros and ones incorrrectly classified _, _, false_negatives, false_positives = model.evaluate(test_dataset, verbose=0) print("-" * 100) @@ -601,7 +799,7 @@ def train_active_learning_models( ) # Loading the best model from this training loop - model = keras.models.load_model("AL_Model.h5") + model = keras.models.load_model("AL_Model.keras") # Plotting the overall history and evaluating the final model plot_history(losses, val_losses, accuracies, val_accuracies) @@ -620,177 +818,779 @@ active_learning_model = train_active_learning_models( ) ``` + +
Model: "sequential_1"
+
+ + + + +
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+│ embedding_1 (Embedding)         │ (None, 150, 128)       │       384,000 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ bidirectional_1 (Bidirectional) │ (None, 150, 64)        │        41,216 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ global_max_pooling1d_1          │ (None, 64)             │             0 │
+│ (GlobalMaxPooling1D)            │                        │               │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dense_2 (Dense)                 │ (None, 20)             │         1,300 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dropout_1 (Dropout)             │ (None, 20)             │             0 │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dense_3 (Dense)                 │ (None, 1)              │            21 │
+└─────────────────────────────────┴────────────────────────┴───────────────┘
+
+ + + + +
 Total params: 426,537 (1.63 MB)
+
+ + + + +
 Trainable params: 426,537 (1.63 MB)
+
+ + + + +
 Non-trainable params: 0 (0.00 B)
+
+ + +
``` -Model: "sequential_1" -_________________________________________________________________ - Layer (type) Output Shape Param # -================================================================= - embedding_1 (Embedding) (None, 150, 128) 384000 - - bidirectional_1 (Bidirectio (None, 150, 64) 41216 - nal) - - global_max_pooling1d_1 (Glo (None, 64) 0 - balMaxPooling1D) - - dense_2 (Dense) (None, 20) 1300 - - dropout_1 (Dropout) (None, 20) 0 - - dense_3 (Dense) (None, 1) 21 - -================================================================= -Total params: 426,537 -Trainable params: 426,537 -Non-trainable params: 0 -_________________________________________________________________ Starting to train with 15000 samples Epoch 1/20 -59/59 [==============================] - ETA: 0s - loss: 0.6235 - binary_accuracy: 0.6679 - false_negatives_1: 3111.0000 - false_positives_1: 1870.0000 -Epoch 00001: val_loss improved from inf to 0.43017, saving model to AL_Model.h5 -59/59 [==============================] - 13s 87ms/step - loss: 0.6235 - binary_accuracy: 0.6679 - false_negatives_1: 3111.0000 - false_positives_1: 1870.0000 - val_loss: 0.4302 - val_binary_accuracy: 0.8286 - val_false_negatives_1: 513.0000 - val_false_positives_1: 344.0000 -Epoch 2/20 -58/59 [============================>.] - ETA: 0s - loss: 0.4381 - binary_accuracy: 0.8232 - false_negatives_1: 1412.0000 - false_positives_1: 1213.0000 -Epoch 00002: val_loss improved from 0.43017 to 0.40090, saving model to AL_Model.h5 -59/59 [==============================] - 4s 64ms/step - loss: 0.4373 - binary_accuracy: 0.8235 - false_negatives_1: 1423.0000 - false_positives_1: 1225.0000 - val_loss: 0.4009 - val_binary_accuracy: 0.8248 - val_false_negatives_1: 674.0000 - val_false_positives_1: 202.0000 -Epoch 3/20 -58/59 [============================>.] - ETA: 0s - loss: 0.3810 - binary_accuracy: 0.8544 - false_negatives_1: 1115.0000 - false_positives_1: 1047.0000 -Epoch 00003: val_loss improved from 0.40090 to 0.36085, saving model to AL_Model.h5 -59/59 [==============================] - 4s 61ms/step - loss: 0.3805 - binary_accuracy: 0.8545 - false_negatives_1: 1123.0000 - false_positives_1: 1060.0000 - val_loss: 0.3608 - val_binary_accuracy: 0.8408 - val_false_negatives_1: 231.0000 - val_false_positives_1: 565.0000 -Epoch 4/20 -58/59 [============================>.] - ETA: 0s - loss: 0.3436 - binary_accuracy: 0.8647 - false_negatives_1: 995.0000 - false_positives_1: 1014.0000 -Epoch 00004: val_loss improved from 0.36085 to 0.35469, saving model to AL_Model.h5 -59/59 [==============================] - 4s 61ms/step - loss: 0.3428 - binary_accuracy: 0.8654 - false_negatives_1: 999.0000 - false_positives_1: 1020.0000 - val_loss: 0.3547 - val_binary_accuracy: 0.8452 - val_false_negatives_1: 266.0000 - val_false_positives_1: 508.0000 -Epoch 5/20 -58/59 [============================>.] - ETA: 0s - loss: 0.3166 - binary_accuracy: 0.8834 - false_negatives_1: 835.0000 - false_positives_1: 897.0000 -Epoch 00005: val_loss did not improve from 0.35469 -59/59 [==============================] - 4s 60ms/step - loss: 0.3163 - binary_accuracy: 0.8835 - false_negatives_1: 839.0000 - false_positives_1: 908.0000 - val_loss: 0.3554 - val_binary_accuracy: 0.8508 - val_false_negatives_1: 382.0000 - val_false_positives_1: 364.0000 -Epoch 6/20 -58/59 [============================>.] - ETA: 0s - loss: 0.2935 - binary_accuracy: 0.8944 - false_negatives_1: 757.0000 - false_positives_1: 811.0000 -Epoch 00006: val_loss did not improve from 0.35469 -59/59 [==============================] - 4s 60ms/step - loss: 0.2938 - binary_accuracy: 0.8945 - false_negatives_1: 765.0000 - false_positives_1: 818.0000 - val_loss: 0.3718 - val_binary_accuracy: 0.8458 - val_false_negatives_1: 345.0000 - val_false_positives_1: 426.0000 -Epoch 7/20 -58/59 [============================>.] - ETA: 0s - loss: 0.2794 - binary_accuracy: 0.9003 - false_negatives_1: 732.0000 - false_positives_1: 748.0000 -Epoch 00007: val_loss did not improve from 0.35469 -59/59 [==============================] - 3s 59ms/step - loss: 0.2797 - binary_accuracy: 0.9001 - false_negatives_1: 749.0000 - false_positives_1: 749.0000 - val_loss: 0.3825 - val_binary_accuracy: 0.8406 - val_false_negatives_1: 228.0000 - val_false_positives_1: 569.0000 -Epoch 8/20 -58/59 [============================>.] - ETA: 0s - loss: 0.2526 - binary_accuracy: 0.9147 - false_negatives_1: 620.0000 - false_positives_1: 647.0000 -Epoch 00008: val_loss did not improve from 0.35469 -59/59 [==============================] - 4s 60ms/step - loss: 0.2561 - binary_accuracy: 0.9134 - false_negatives_1: 620.0000 - false_positives_1: 679.0000 - val_loss: 0.4109 - val_binary_accuracy: 0.8258 - val_false_negatives_1: 622.0000 - val_false_positives_1: 249.0000 -Epoch 00008: early stopping ----------------------------------------------------------------------------------------------------- -Number of zeros incorrectly classified: 665.0, Number of ones incorrectly classified: 234.0 -Sample ratio for positives: 0.26028921023359286, Sample ratio for negatives:0.7397107897664071 -Starting training with 19999 samples ----------------------------------------------------------------------------------------------------- -Epoch 1/20 -78/79 [============================>.] - ETA: 0s - loss: 0.2955 - binary_accuracy: 0.8902 - false_negatives_2: 1091.0000 - false_positives_2: 1101.0000 -Epoch 00001: val_loss did not improve from 0.35469 -79/79 [==============================] - 15s 83ms/step - loss: 0.2956 - binary_accuracy: 0.8901 - false_negatives_2: 1095.0000 - false_positives_2: 1102.0000 - val_loss: 0.4136 - val_binary_accuracy: 0.8238 - val_false_negatives_2: 156.0000 - val_false_positives_2: 725.0000 -Epoch 2/20 -78/79 [============================>.] - ETA: 0s - loss: 0.2657 - binary_accuracy: 0.9047 - false_negatives_2: 953.0000 - false_positives_2: 949.0000 -Epoch 00002: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 61ms/step - loss: 0.2659 - binary_accuracy: 0.9047 - false_negatives_2: 954.0000 - false_positives_2: 951.0000 - val_loss: 0.4079 - val_binary_accuracy: 0.8386 - val_false_negatives_2: 510.0000 - val_false_positives_2: 297.0000 -Epoch 3/20 -78/79 [============================>.] - ETA: 0s - loss: 0.2475 - binary_accuracy: 0.9126 - false_negatives_2: 892.0000 - false_positives_2: 854.0000 -Epoch 00003: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 58ms/step - loss: 0.2474 - binary_accuracy: 0.9126 - false_negatives_2: 893.0000 - false_positives_2: 855.0000 - val_loss: 0.4207 - val_binary_accuracy: 0.8364 - val_false_negatives_2: 228.0000 - val_false_positives_2: 590.0000 -Epoch 4/20 -78/79 [============================>.] - ETA: 0s - loss: 0.2319 - binary_accuracy: 0.9193 - false_negatives_2: 805.0000 - false_positives_2: 807.0000 -Epoch 00004: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.2319 - binary_accuracy: 0.9192 - false_negatives_2: 807.0000 - false_positives_2: 808.0000 - val_loss: 0.4080 - val_binary_accuracy: 0.8310 - val_false_negatives_2: 264.0000 - val_false_positives_2: 581.0000 -Epoch 5/20 -78/79 [============================>.] - ETA: 0s - loss: 0.2133 - binary_accuracy: 0.9260 - false_negatives_2: 728.0000 - false_positives_2: 750.0000 -Epoch 00005: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.2133 - binary_accuracy: 0.9259 - false_negatives_2: 729.0000 - false_positives_2: 752.0000 - val_loss: 0.4054 - val_binary_accuracy: 0.8394 - val_false_negatives_2: 371.0000 - val_false_positives_2: 432.0000 -Epoch 6/20 -78/79 [============================>.] - ETA: 0s - loss: 0.1982 - binary_accuracy: 0.9361 - false_negatives_2: 639.0000 - false_positives_2: 636.0000 -Epoch 00006: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.1980 - binary_accuracy: 0.9362 - false_negatives_2: 639.0000 - false_positives_2: 636.0000 - val_loss: 0.5185 - val_binary_accuracy: 0.8284 - val_false_negatives_2: 590.0000 - val_false_positives_2: 268.0000 -Epoch 7/20 -78/79 [============================>.] - ETA: 0s - loss: 0.1887 - binary_accuracy: 0.9409 - false_negatives_2: 606.0000 - false_positives_2: 575.0000 -Epoch 00007: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.1886 - binary_accuracy: 0.9408 - false_negatives_2: 606.0000 - false_positives_2: 577.0000 - val_loss: 0.6881 - val_binary_accuracy: 0.7886 - val_false_negatives_2: 893.0000 - val_false_positives_2: 164.0000 -Epoch 8/20 -78/79 [============================>.] - ETA: 0s - loss: 0.1778 - binary_accuracy: 0.9443 - false_negatives_2: 575.0000 - false_positives_2: 538.0000 -Epoch 00008: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.1776 - binary_accuracy: 0.9443 - false_negatives_2: 575.0000 - false_positives_2: 538.0000 - val_loss: 0.5921 - val_binary_accuracy: 0.8244 - val_false_negatives_2: 634.0000 - val_false_positives_2: 244.0000 -Epoch 9/20 -78/79 [============================>.] - ETA: 0s - loss: 0.1598 - binary_accuracy: 0.9505 - false_negatives_2: 507.0000 - false_positives_2: 481.0000 -Epoch 00009: val_loss did not improve from 0.35469 -79/79 [==============================] - 5s 57ms/step - loss: 0.1597 - binary_accuracy: 0.9506 - false_negatives_2: 507.0000 - false_positives_2: 481.0000 - val_loss: 0.5393 - val_binary_accuracy: 0.8214 - val_false_negatives_2: 542.0000 - val_false_positives_2: 351.0000 -Epoch 00009: early stopping ----------------------------------------------------------------------------------------------------- -Number of zeros incorrectly classified: 270.0, Number of ones incorrectly classified: 498.0 -Sample ratio for positives: 0.6484375, Sample ratio for negatives:0.3515625 -Starting training with 24998 samples ----------------------------------------------------------------------------------------------------- -Epoch 1/20 -97/98 [============================>.] - ETA: 0s - loss: 0.3554 - binary_accuracy: 0.8609 - false_negatives_3: 1714.0000 - false_positives_3: 1739.0000 -Epoch 00001: val_loss improved from 0.35469 to 0.34182, saving model to AL_Model.h5 -98/98 [==============================] - 17s 82ms/step - loss: 0.3548 - binary_accuracy: 0.8613 - false_negatives_3: 1720.0000 - false_positives_3: 1748.0000 - val_loss: 0.3418 - val_binary_accuracy: 0.8528 - val_false_negatives_3: 369.0000 - val_false_positives_3: 367.0000 -Epoch 2/20 -97/98 [============================>.] - ETA: 0s - loss: 0.3176 - binary_accuracy: 0.8785 - false_negatives_3: 1473.0000 - false_positives_3: 1544.0000 -Epoch 00002: val_loss did not improve from 0.34182 -98/98 [==============================] - 6s 56ms/step - loss: 0.3179 - binary_accuracy: 0.8784 - false_negatives_3: 1479.0000 - false_positives_3: 1560.0000 - val_loss: 0.4785 - val_binary_accuracy: 0.8102 - val_false_negatives_3: 793.0000 - val_false_positives_3: 156.0000 -Epoch 3/20 -97/98 [============================>.] - ETA: 0s - loss: 0.2986 - binary_accuracy: 0.8893 - false_negatives_3: 1353.0000 - false_positives_3: 1396.0000 -Epoch 00003: val_loss did not improve from 0.34182 -98/98 [==============================] - 5s 56ms/step - loss: 0.2985 - binary_accuracy: 0.8893 - false_negatives_3: 1366.0000 - false_positives_3: 1402.0000 - val_loss: 0.3473 - val_binary_accuracy: 0.8542 - val_false_negatives_3: 340.0000 - val_false_positives_3: 389.0000 -Epoch 4/20 -97/98 [============================>.] - ETA: 0s - loss: 0.2822 - binary_accuracy: 0.8970 - false_negatives_3: 1253.0000 - false_positives_3: 1305.0000 -Epoch 00004: val_loss did not improve from 0.34182 -98/98 [==============================] - 6s 56ms/step - loss: 0.2820 - binary_accuracy: 0.8971 - false_negatives_3: 1257.0000 - false_positives_3: 1316.0000 - val_loss: 0.3849 - val_binary_accuracy: 0.8386 - val_false_negatives_3: 537.0000 - val_false_positives_3: 270.0000 -Epoch 5/20 -97/98 [============================>.] - ETA: 0s - loss: 0.2666 - binary_accuracy: 0.9047 - false_negatives_3: 1130.0000 - false_positives_3: 1237.0000 -Epoch 00005: val_loss did not improve from 0.34182 -98/98 [==============================] - 6s 56ms/step - loss: 0.2666 - binary_accuracy: 0.9048 - false_negatives_3: 1142.0000 - false_positives_3: 1238.0000 - val_loss: 0.3731 - val_binary_accuracy: 0.8444 - val_false_negatives_3: 251.0000 - val_false_positives_3: 527.0000 -Epoch 00005: early stopping ----------------------------------------------------------------------------------------------------- -Number of zeros incorrectly classified: 392.0, Number of ones incorrectly classified: 356.0 -Sample ratio for positives: 0.47593582887700536, Sample ratio for negatives:0.5240641711229946 -Starting training with 29997 samples ----------------------------------------------------------------------------------------------------- -Epoch 1/20 -117/118 [============================>.] - ETA: 0s - loss: 0.3345 - binary_accuracy: 0.8720 - false_negatives_4: 1835.0000 - false_positives_4: 1998.0000 -Epoch 00001: val_loss did not improve from 0.34182 -118/118 [==============================] - 20s 96ms/step - loss: 0.3343 - binary_accuracy: 0.8722 - false_negatives_4: 1835.0000 - false_positives_4: 1999.0000 - val_loss: 0.3478 - val_binary_accuracy: 0.8488 - val_false_negatives_4: 250.0000 - val_false_positives_4: 506.0000 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.5197 - false_negatives_1: 1686.7457 - false_positives_1: 1938.3051 - loss: 0.6918 + + +
+``` +Epoch 1: val_loss improved from inf to 0.67428, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - binary_accuracy: 0.5202 - false_negatives_1: 1716.9833 - false_positives_1: 1961.4667 - loss: 0.6917 - val_binary_accuracy: 0.6464 - val_false_negatives_1: 279.0000 - val_false_positives_1: 1489.0000 - val_loss: 0.6743 + + +
+``` Epoch 2/20 -117/118 [============================>.] - ETA: 0s - loss: 0.3061 - binary_accuracy: 0.8842 - false_negatives_4: 1667.0000 - false_positives_4: 1801.0000 -Epoch 00002: val_loss improved from 0.34182 to 0.33779, saving model to AL_Model.h5 -118/118 [==============================] - 7s 56ms/step - loss: 0.3059 - binary_accuracy: 0.8843 - false_negatives_4: 1670.0000 - false_positives_4: 1802.0000 - val_loss: 0.3378 - val_binary_accuracy: 0.8534 - val_false_negatives_4: 335.0000 - val_false_positives_4: 398.0000 -Epoch 3/20 -117/118 [============================>.] - ETA: 0s - loss: 0.2923 - binary_accuracy: 0.8921 - false_negatives_4: 1626.0000 - false_positives_4: 1607.0000 -Epoch 00003: val_loss did not improve from 0.33779 -118/118 [==============================] - 7s 56ms/step - loss: 0.2923 - binary_accuracy: 0.8921 - false_negatives_4: 1626.0000 - false_positives_4: 1611.0000 - val_loss: 0.3413 - val_binary_accuracy: 0.8486 - val_false_negatives_4: 269.0000 - val_false_positives_4: 488.0000 -Epoch 4/20 -117/118 [============================>.] - ETA: 0s - loss: 0.2746 - binary_accuracy: 0.8997 - false_negatives_4: 1459.0000 - false_positives_4: 1546.0000 -Epoch 00004: val_loss did not improve from 0.33779 -118/118 [==============================] - 7s 55ms/step - loss: 0.2746 - binary_accuracy: 0.8996 - false_negatives_4: 1465.0000 - false_positives_4: 1546.0000 - val_loss: 0.3810 - val_binary_accuracy: 0.8326 - val_false_negatives_4: 169.0000 - val_false_positives_4: 668.0000 -Epoch 5/20 -117/118 [============================>.] - ETA: 0s - loss: 0.2598 - binary_accuracy: 0.9066 - false_negatives_4: 1336.0000 - false_positives_4: 1462.0000 -Epoch 00005: val_loss did not improve from 0.33779 -118/118 [==============================] - 7s 56ms/step - loss: 0.2597 - binary_accuracy: 0.9066 - false_negatives_4: 1337.0000 - false_positives_4: 1465.0000 - val_loss: 0.4038 - val_binary_accuracy: 0.8332 - val_false_negatives_4: 643.0000 - val_false_positives_4: 191.0000 -Epoch 6/20 -117/118 [============================>.] - ETA: 0s - loss: 0.2461 - binary_accuracy: 0.9132 - false_negatives_4: 1263.0000 - false_positives_4: 1337.0000 -Epoch 00006: val_loss did not improve from 0.33779 -118/118 [==============================] - 7s 55ms/step - loss: 0.2462 - binary_accuracy: 0.9132 - false_negatives_4: 1263.0000 - false_positives_4: 1341.0000 - val_loss: 0.3546 - val_binary_accuracy: 0.8500 - val_false_negatives_4: 359.0000 - val_false_positives_4: 391.0000 -Epoch 00006: early stopping ```
-![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_17_1.png) + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.6505 - false_negatives_1: 1216.0170 - false_positives_1: 1434.2373 - loss: 0.6561 + +
+``` +Epoch 2: val_loss improved from 0.67428 to 0.59133, saving model to AL_Model.keras -![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_17_2.png) + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.6507 - false_negatives_1: 1234.9833 - false_positives_1: 1455.7667 - loss: 0.6558 - val_binary_accuracy: 0.7032 - val_false_negatives_1: 235.0000 - val_false_positives_1: 1249.0000 - val_loss: 0.5913
``` ----------------------------------------------------------------------------------------------------- -Test set evaluation: {'loss': 0.34248775243759155, 'binary_accuracy': 0.854200005531311, 'false_negatives_4': 348.0, 'false_positives_4': 381.0} +Epoch 3/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.7103 - false_negatives_1: 939.5255 - false_positives_1: 1235.8983 - loss: 0.5829 + + +
+``` +Epoch 3: val_loss improved from 0.59133 to 0.51602, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.7106 - false_negatives_1: 953.0500 - false_positives_1: 1255.3167 - loss: 0.5827 - val_binary_accuracy: 0.7686 - val_false_negatives_1: 812.0000 - val_false_positives_1: 345.0000 - val_loss: 0.5160 + + +
+``` +Epoch 4/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.7545 - false_negatives_1: 787.4237 - false_positives_1: 1070.0339 - loss: 0.5214 + + +
+``` +Epoch 4: val_loss improved from 0.51602 to 0.43948, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.7547 - false_negatives_1: 799.2667 - false_positives_1: 1085.8833 - loss: 0.5212 - val_binary_accuracy: 0.8028 - val_false_negatives_1: 342.0000 - val_false_positives_1: 644.0000 - val_loss: 0.4395 + + +
+``` +Epoch 5/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.7919 - false_negatives_1: 676.7458 - false_positives_1: 907.4915 - loss: 0.4657 + + +
+``` +Epoch 5: val_loss improved from 0.43948 to 0.41679, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.7920 - false_negatives_1: 687.3834 - false_positives_1: 921.1667 - loss: 0.4655 - val_binary_accuracy: 0.8158 - val_false_negatives_1: 598.0000 - val_false_positives_1: 323.0000 - val_loss: 0.4168 + + +
+``` +Epoch 6/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.7994 - false_negatives_1: 661.3560 - false_positives_1: 828.0847 - loss: 0.4498 + + +
+``` +Epoch 6: val_loss improved from 0.41679 to 0.39680, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.7997 - false_negatives_1: 671.3666 - false_positives_1: 840.2500 - loss: 0.4495 - val_binary_accuracy: 0.8260 - val_false_negatives_1: 382.0000 - val_false_positives_1: 488.0000 - val_loss: 0.3968 + + +
+``` +Epoch 7/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8311 - false_negatives_1: 589.1187 - false_positives_1: 707.0170 - loss: 0.4017 + + +
+``` +Epoch 7: val_loss did not improve from 0.39680 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8312 - false_negatives_1: 598.3500 - false_positives_1: 717.8167 - loss: 0.4016 - val_binary_accuracy: 0.7706 - val_false_negatives_1: 1004.0000 - val_false_positives_1: 143.0000 - val_loss: 0.4884 + + +
+``` +Epoch 8/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8365 - false_negatives_1: 566.7288 - false_positives_1: 649.9322 - loss: 0.3896 + + +
+``` +Epoch 8: val_loss did not improve from 0.39680 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8366 - false_negatives_1: 575.2833 - false_positives_1: 660.2167 - loss: 0.3895 - val_binary_accuracy: 0.8216 - val_false_negatives_1: 623.0000 - val_false_positives_1: 269.0000 - val_loss: 0.4043 + + +
+``` +Epoch 9/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8531 - false_negatives_1: 519.0170 - false_positives_1: 591.6440 - loss: 0.3631 + + +
+``` +Epoch 9: val_loss improved from 0.39680 to 0.37727, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8531 - false_negatives_1: 527.2667 - false_positives_1: 601.2500 - loss: 0.3631 - val_binary_accuracy: 0.8348 - val_false_negatives_1: 296.0000 - val_false_positives_1: 530.0000 - val_loss: 0.3773 + + +
+``` +Epoch 10/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8686 - false_negatives_1: 475.7966 - false_positives_1: 569.0508 - loss: 0.3387 + + +
+``` +Epoch 10: val_loss improved from 0.37727 to 0.37354, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8685 - false_negatives_1: 483.5000 - false_positives_1: 577.9667 - loss: 0.3387 - val_binary_accuracy: 0.8400 - val_false_negatives_1: 327.0000 - val_false_positives_1: 473.0000 - val_loss: 0.3735 + + +
+``` +Epoch 11/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8716 - false_negatives_1: 452.1356 - false_positives_1: 522.1187 - loss: 0.3303 + + +
+``` +Epoch 11: val_loss improved from 0.37354 to 0.37074, saving model to AL_Model.keras + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8716 - false_negatives_1: 459.3833 - false_positives_1: 530.6667 - loss: 0.3303 - val_binary_accuracy: 0.8390 - val_false_negatives_1: 362.0000 - val_false_positives_1: 443.0000 - val_loss: 0.3707 + + +
+``` +Epoch 12/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8833 - false_negatives_1: 433.0678 - false_positives_1: 481.1864 - loss: 0.3065 + + +
+``` +Epoch 12: val_loss did not improve from 0.37074 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8833 - false_negatives_1: 439.8333 - false_positives_1: 488.9667 - loss: 0.3066 - val_binary_accuracy: 0.8236 - val_false_negatives_1: 208.0000 - val_false_positives_1: 674.0000 - val_loss: 0.4046 + + +
+``` +Epoch 13/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8876 - false_negatives_1: 384.8305 - false_positives_1: 476.5254 - loss: 0.2978 + + +
+``` +Epoch 13: val_loss did not improve from 0.37074 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 82ms/step - binary_accuracy: 0.8876 - false_negatives_1: 391.2667 - false_positives_1: 484.2500 - loss: 0.2978 - val_binary_accuracy: 0.8380 - val_false_negatives_1: 364.0000 - val_false_positives_1: 446.0000 - val_loss: 0.3783 + + +
+``` +Epoch 14/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8976 - false_negatives_1: 378.0169 - false_positives_1: 433.9831 - loss: 0.2754 + + +
+``` +Epoch 14: val_loss did not improve from 0.37074 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.8975 - false_negatives_1: 384.2333 - false_positives_1: 441.3833 - loss: 0.2757 - val_binary_accuracy: 0.8310 - val_false_negatives_1: 525.0000 - val_false_positives_1: 320.0000 - val_loss: 0.3957 + + +
+``` +Epoch 15/20 + +``` +
+ + 59/59 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9013 - false_negatives_1: 354.9322 - false_positives_1: 403.1695 - loss: 0.2709 + + +
+``` +Epoch 15: val_loss did not improve from 0.37074 + + +``` +
+ 59/59 ━━━━━━━━━━━━━━━━━━━━ 5s 83ms/step - binary_accuracy: 0.9013 - false_negatives_1: 360.4000 - false_positives_1: 409.5833 - loss: 0.2709 - val_binary_accuracy: 0.8298 - val_false_negatives_1: 302.0000 - val_false_positives_1: 549.0000 - val_loss: 0.4015 + + +
+``` +Epoch 15: early stopping + +``` +
+ + 20/20 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step + + +
+``` +---------------------------------------------------------------------------------------------------- +Number of zeros incorrectly classified: 290.0, Number of ones incorrectly classified: 538.0 +Sample ratio for positives: 0.6497584541062802, Sample ratio for negatives:0.3502415458937198 +Starting training with 19999 samples +---------------------------------------------------------------------------------------------------- +Epoch 1/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8735 - false_negatives_2: 547.2436 - false_positives_2: 650.2436 - loss: 0.3527 + + +
+``` +Epoch 1: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - binary_accuracy: 0.8738 - false_negatives_2: 559.2125 - false_positives_2: 665.3375 - loss: 0.3518 - val_binary_accuracy: 0.7932 - val_false_negatives_2: 119.0000 - val_false_positives_2: 915.0000 - val_loss: 0.4949 + + +
+``` +Epoch 2/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8961 - false_negatives_2: 470.2436 - false_positives_2: 576.1539 - loss: 0.2824 + + +
+``` +Epoch 2: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 80ms/step - binary_accuracy: 0.8962 - false_negatives_2: 481.4125 - false_positives_2: 589.6750 - loss: 0.2823 - val_binary_accuracy: 0.8014 - val_false_negatives_2: 809.0000 - val_false_positives_2: 184.0000 - val_loss: 0.4580 + + +
+``` +Epoch 3/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9059 - false_negatives_2: 442.2051 - false_positives_2: 500.5385 - loss: 0.2628 + + +
+``` +Epoch 3: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 80ms/step - binary_accuracy: 0.9059 - false_negatives_2: 452.6750 - false_positives_2: 513.5250 - loss: 0.2629 - val_binary_accuracy: 0.8294 - val_false_negatives_2: 302.0000 - val_false_positives_2: 551.0000 - val_loss: 0.3868 + + +
+``` +Epoch 4/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9188 - false_negatives_2: 394.5513 - false_positives_2: 462.4359 - loss: 0.2391 + + +
+``` +Epoch 4: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 80ms/step - binary_accuracy: 0.9187 - false_negatives_2: 405.0625 - false_positives_2: 474.1250 - loss: 0.2393 - val_binary_accuracy: 0.8268 - val_false_negatives_2: 225.0000 - val_false_positives_2: 641.0000 - val_loss: 0.4197 + + +
+``` +Epoch 5/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9255 - false_negatives_2: 349.8718 - false_positives_2: 413.0898 - loss: 0.2270 + + +
+``` +Epoch 5: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 79ms/step - binary_accuracy: 0.9254 - false_negatives_2: 358.6500 - false_positives_2: 423.5625 - loss: 0.2270 - val_binary_accuracy: 0.8228 - val_false_negatives_2: 611.0000 - val_false_positives_2: 275.0000 - val_loss: 0.4233 + + +
+``` +Epoch 6/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 73ms/step - binary_accuracy: 0.9265 - false_negatives_2: 349.8590 - false_positives_2: 389.9359 - loss: 0.2147 + + +
+``` +Epoch 6: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 80ms/step - binary_accuracy: 0.9265 - false_negatives_2: 358.8375 - false_positives_2: 399.9875 - loss: 0.2148 - val_binary_accuracy: 0.8272 - val_false_negatives_2: 581.0000 - val_false_positives_2: 283.0000 - val_loss: 0.4415 + + +
+``` +Epoch 7/20 + +``` +
+ + 78/79 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.9409 - false_negatives_2: 286.7820 - false_positives_2: 322.7949 - loss: 0.1877 + + +
+``` +Epoch 7: val_loss did not improve from 0.37074 + + +``` +
+ 79/79 ━━━━━━━━━━━━━━━━━━━━ 6s 79ms/step - binary_accuracy: 0.9408 - false_negatives_2: 294.4375 - false_positives_2: 331.4000 - loss: 0.1880 - val_binary_accuracy: 0.8266 - val_false_negatives_2: 528.0000 - val_false_positives_2: 339.0000 - val_loss: 0.4419 + + +
+``` +Epoch 7: early stopping + +``` +
+ + 20/20 ━━━━━━━━━━━━━━━━━━━━ 1s 39ms/step + + +
+``` +---------------------------------------------------------------------------------------------------- +Number of zeros incorrectly classified: 376.0, Number of ones incorrectly classified: 442.0 +Sample ratio for positives: 0.5403422982885085, Sample ratio for negatives:0.45965770171149145 +Starting training with 24998 samples +---------------------------------------------------------------------------------------------------- +Epoch 1/20 + +``` +
+ + 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 73ms/step - binary_accuracy: 0.8509 - false_negatives_3: 809.9184 - false_positives_3: 1018.9286 - loss: 0.3732 + + +
+``` +Epoch 1: val_loss improved from 0.37074 to 0.36196, saving model to AL_Model.keras + + +``` +
+ 98/98 ━━━━━━━━━━━━━━━━━━━━ 11s 83ms/step - binary_accuracy: 0.8509 - false_negatives_3: 817.5757 - false_positives_3: 1028.7980 - loss: 0.3731 - val_binary_accuracy: 0.8424 - val_false_negatives_3: 368.0000 - val_false_positives_3: 420.0000 - val_loss: 0.3620 + + +
+``` +Epoch 2/20 + +``` +
+ + 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8744 - false_negatives_3: 734.7449 - false_positives_3: 884.7755 - loss: 0.3185 + + +
+``` +Epoch 2: val_loss did not improve from 0.36196 + + +``` +
+ 98/98 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - binary_accuracy: 0.8744 - false_negatives_3: 741.9697 - false_positives_3: 893.7172 - loss: 0.3186 - val_binary_accuracy: 0.8316 - val_false_negatives_3: 202.0000 - val_false_positives_3: 640.0000 - val_loss: 0.3792 + + +
+``` +Epoch 3/20 + +``` +
+ + 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8830 - false_negatives_3: 684.1326 - false_positives_3: 807.8878 - loss: 0.3090 + + +
+``` +Epoch 3: val_loss did not improve from 0.36196 + + +``` +
+ 98/98 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - binary_accuracy: 0.8830 - false_negatives_3: 691.0707 - false_positives_3: 816.2222 - loss: 0.3090 - val_binary_accuracy: 0.8118 - val_false_negatives_3: 738.0000 - val_false_positives_3: 203.0000 - val_loss: 0.4112 + + +
+``` +Epoch 4/20 + +``` +
+ + 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8892 - false_negatives_3: 651.9898 - false_positives_3: 776.4388 - loss: 0.2928 + + +
+``` +Epoch 4: val_loss did not improve from 0.36196 + + +``` +
+ 98/98 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - binary_accuracy: 0.8892 - false_negatives_3: 658.4041 - false_positives_3: 784.3839 - loss: 0.2928 - val_binary_accuracy: 0.8344 - val_false_negatives_3: 557.0000 - val_false_positives_3: 271.0000 - val_loss: 0.3734 + + +
+``` +Epoch 5/20 + +``` +
+ + 98/98 ━━━━━━━━━━━━━━━━━━━━ 0s 72ms/step - binary_accuracy: 0.8975 - false_negatives_3: 612.0714 - false_positives_3: 688.9184 - loss: 0.2806 + + +
+``` +Epoch 5: val_loss did not improve from 0.36196 + + +``` +
+ 98/98 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - binary_accuracy: 0.8974 - false_negatives_3: 618.4343 - false_positives_3: 696.1313 - loss: 0.2807 - val_binary_accuracy: 0.8456 - val_false_negatives_3: 446.0000 - val_false_positives_3: 326.0000 - val_loss: 0.3658 + + +
+``` +Epoch 5: early stopping + +``` +
+ + 20/20 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step + + +
+``` +---------------------------------------------------------------------------------------------------- +Number of zeros incorrectly classified: 407.0, Number of ones incorrectly classified: 410.0 +Sample ratio for positives: 0.5018359853121175, Sample ratio for negatives:0.4981640146878825 +Starting training with 29997 samples +---------------------------------------------------------------------------------------------------- +Epoch 1/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step - binary_accuracy: 0.8621 - false_negatives_4: 916.2393 - false_positives_4: 1130.9744 - loss: 0.3527 + + +
+``` +Epoch 1: val_loss did not improve from 0.36196 + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 13s 85ms/step - binary_accuracy: 0.8621 - false_negatives_4: 931.0924 - false_positives_4: 1149.7479 - loss: 0.3525 - val_binary_accuracy: 0.8266 - val_false_negatives_4: 627.0000 - val_false_positives_4: 240.0000 - val_loss: 0.3802 + + +
+``` +Epoch 2/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step - binary_accuracy: 0.8761 - false_negatives_4: 876.4872 - false_positives_4: 1005.5726 - loss: 0.3195 + + +
+``` +Epoch 2: val_loss improved from 0.36196 to 0.35707, saving model to AL_Model.keras + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - binary_accuracy: 0.8760 - false_negatives_4: 891.0504 - false_positives_4: 1022.9412 - loss: 0.3196 - val_binary_accuracy: 0.8404 - val_false_negatives_4: 479.0000 - val_false_positives_4: 319.0000 - val_loss: 0.3571 + + +
+``` +Epoch 3/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 74ms/step - binary_accuracy: 0.8874 - false_negatives_4: 801.1710 - false_positives_4: 941.4786 - loss: 0.2965 + + +
+``` +Epoch 3: val_loss did not improve from 0.35707 + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 9s 79ms/step - binary_accuracy: 0.8873 - false_negatives_4: 814.8319 - false_positives_4: 957.8571 - loss: 0.2966 - val_binary_accuracy: 0.8226 - val_false_negatives_4: 677.0000 - val_false_positives_4: 210.0000 - val_loss: 0.3948 + + +
+``` +Epoch 4/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step - binary_accuracy: 0.8977 - false_negatives_4: 740.5385 - false_positives_4: 837.1710 - loss: 0.2768 + + +
+``` +Epoch 4: val_loss did not improve from 0.35707 + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 10s 81ms/step - binary_accuracy: 0.8976 - false_negatives_4: 753.5378 - false_positives_4: 852.2437 - loss: 0.2770 - val_binary_accuracy: 0.8406 - val_false_negatives_4: 530.0000 - val_false_positives_4: 267.0000 - val_loss: 0.3630 + + +
+``` +Epoch 5/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step - binary_accuracy: 0.9020 - false_negatives_4: 722.5214 - false_positives_4: 808.2308 - loss: 0.2674 + + +
+``` +Epoch 5: val_loss did not improve from 0.35707 + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - binary_accuracy: 0.9019 - false_negatives_4: 734.8655 - false_positives_4: 822.4117 - loss: 0.2676 - val_binary_accuracy: 0.8330 - val_false_negatives_4: 592.0000 - val_false_positives_4: 243.0000 - val_loss: 0.3805 + + +
+``` +Epoch 6/20 + +``` +
+ + 117/118 ━━━━━━━━━━━━━━━━━━━━ 0s 76ms/step - binary_accuracy: 0.9059 - false_negatives_4: 682.1453 - false_positives_4: 737.0513 - loss: 0.2525 + + +
+``` +Epoch 6: val_loss did not improve from 0.35707 + + +``` +
+ 118/118 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - binary_accuracy: 0.9059 - false_negatives_4: 693.6387 - false_positives_4: 749.9412 - loss: 0.2526 - val_binary_accuracy: 0.8454 - val_false_negatives_4: 391.0000 - val_false_positives_4: 382.0000 - val_loss: 0.3620 + + +
+``` +Epoch 6: early stopping + +``` +
+ +![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_17_2767.png) + + + + + +![png](/img/examples/nlp/active_learning_review_classification/active_learning_review_classification_17_2768.png) + + + +
+``` +---------------------------------------------------------------------------------------------------- + +Test set evaluation: {'binary_accuracy': 0.8424000144004822, 'false_negatives_4': 491.0, 'false_positives_4': 297.0, 'loss': 0.3661557137966156} ---------------------------------------------------------------------------------------------------- ```