 在 TensorFlow.org 上檢視
|
 在 Google Colab 中執行
|
 在 GitHub 上檢視
|
 下載筆記本
|
 查看 TF Hub 模型
|
歡迎使用 TensorFlow Decision Forests (TF-DF) 的中階 Colab。在本 Colab 中，您將學習 TF-DF 的一些更進階功能，包括如何處理自然語言特徵。
本 Colab 假設您已熟悉初階 Colab中介紹的概念，特別是有關 TF-DF 的安裝。
在本 Colab 中，您將
訓練一個原生使用文字特徵作為類別集合的隨機森林。
訓練一個使用 TensorFlow Hub 模組來使用文字特徵的隨機森林。在此設定 (轉移學習) 中，模組已在大型文字語料庫上預先訓練。
一起訓練梯度提升決策樹 (GBDT) 和神經網路。GBDT 將使用神經網路的輸出。
設定
# Install TensorFlow Dececision Forestspip install tensorflow_decision_forests
需要 Wurlitzer 才能在 Colab 中顯示詳細的訓練記錄 (當在模型建構函式中使用 verbose=2 時)。
pip install wurlitzer
匯入必要的程式庫。
import os
# Keep using Keras 2
os.environ['TF_USE_LEGACY_KERAS'] = '1'
import tensorflow_decision_forests as tfdf
import numpy as np
import pandas as pd
import tensorflow as tf
import tf_keras
import math
隱藏的程式碼儲存格限制了 Colab 中的輸出高度。
使用原始文字作為特徵
TF-DF 可以原生使用類別集合特徵。類別集合將文字特徵表示為詞袋 (或 n-gram)。
例如："The little blue dog" → {"the", "little", "blue", "dog"}
在此範例中，您將在史丹佛情感樹庫 (SST) 資料集上訓練隨機森林。此資料集的目標是將句子分類為帶有正面或負面情感。您將使用 TensorFlow Datasets 中整理的資料集二元分類版本。
# Install the TensorFlow Datasets packagepip install tensorflow-datasets -U --quiet
# Load the dataset
import tensorflow_datasets as tfds
all_ds = tfds.load("glue/sst2")
# Display the first 3 examples of the test fold.
for example in all_ds["test"].take(3):
print({attr_name: attr_tensor.numpy() for attr_name, attr_tensor in example.items()})
{'idx': 163, 'label': -1, 'sentence': b'not even the hanson brothers can save it'}
{'idx': 131, 'label': -1, 'sentence': b'strong setup and ambitious goals fade as the film descends into unsophisticated scare tactics and b-film thuggery .'}
{'idx': 1579, 'label': -1, 'sentence': b'too timid to bring a sense of closure to an ugly chapter of the twentieth century .'}
2024-04-20 11:17:31.940774: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
2024-04-20 11:17:31.946131: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
資料集修改如下
- 原始標籤是
{-1, 1}中的整數，但學習演算法預期為正整數標籤，例如{0, 1}。因此，標籤轉換如下：new_labels = (original_labels + 1) / 2。 - 套用批次大小 64 以提高資料集讀取效率。
sentence屬性需要進行符號化，即"hello world" -> ["hello", "world"]。
詳細資訊： 有些決策森林學習演算法不需要驗證資料集 (例如隨機森林)，而其他演算法則需要 (例如某些情況下的梯度提升樹)。由於 TF-DF 下的每個學習演算法都可以不同方式使用驗證資料，因此 TF-DF 會在內部處理訓練/驗證分割。因此，當您有訓練集和驗證集時，它們始終可以串連起來作為學習演算法的輸入。
def prepare_dataset(example):
label = (example["label"] + 1) // 2
return {"sentence" : tf.strings.split(example["sentence"])}, label
train_ds = all_ds["train"].batch(100).map(prepare_dataset)
test_ds = all_ds["validation"].batch(100).map(prepare_dataset)
最後，照常訓練和評估模型。TF-DF 會自動將多值類別特徵偵測為類別集合。
%set_cell_height 300
# Specify the model.
model_1 = tfdf.keras.RandomForestModel(num_trees=30, verbose=2)
# Train the model.
model_1.fit(x=train_ds)
<IPython.core.display.Javascript object>
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
Use /tmpfs/tmp/tmpx28adgyq as temporary training directory
Reading training dataset...
Training tensor examples:
Features: {'sentence': tf.RaggedTensor(values=Tensor("data:0", shape=(None,), dtype=string), row_splits=Tensor("data_1:0", shape=(None,), dtype=int64))}
Label: Tensor("data_2:0", shape=(None,), dtype=int64)
Weights: None
Normalized tensor features:
{'sentence': SemanticTensor(semantic=<Semantic.CATEGORICAL_SET: 4>, tensor=tf.RaggedTensor(values=Tensor("data:0", shape=(None,), dtype=string), row_splits=Tensor("data_1:0", shape=(None,), dtype=int64)))}
Training dataset read in 0:00:04.709443. Found 67349 examples.
Training model...
Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).
[INFO 24-04-20 11:17:36.8175 UTC kernel.cc:771] Start Yggdrasil model training
[INFO 24-04-20 11:17:36.8176 UTC kernel.cc:772] Collect training examples
[INFO 24-04-20 11:17:36.8176 UTC kernel.cc:785] Dataspec guide:
column_guides {
column_name_pattern: "^__LABEL$"
type: CATEGORICAL
categorial {
min_vocab_frequency: 0
max_vocab_count: -1
}
}
default_column_guide {
categorial {
max_vocab_count: 2000
}
discretized_numerical {
maximum_num_bins: 255
}
}
ignore_columns_without_guides: false
detect_numerical_as_discretized_numerical: false
[INFO 24-04-20 11:17:36.8179 UTC kernel.cc:391] Number of batches: 674
[INFO 24-04-20 11:17:36.8180 UTC kernel.cc:392] Number of examples: 67349
[INFO 24-04-20 11:17:36.8602 UTC data_spec_inference.cc:305] 12816 item(s) have been pruned (i.e. they are considered out of dictionary) for the column sentence (2000 item(s) left) because min_value_count=5 and max_number_of_unique_values=2000
[INFO 24-04-20 11:17:36.9136 UTC kernel.cc:792] Training dataset:
Number of records: 67349
Number of columns: 2
Number of columns by type:
CATEGORICAL_SET: 1 (50%)
CATEGORICAL: 1 (50%)
Columns:
CATEGORICAL_SET: 1 (50%)
1: "sentence" CATEGORICAL_SET has-dict vocab-size:2001 num-oods:10187 (15.1257%) most-frequent:"the" 27205 (40.3941%)
CATEGORICAL: 1 (50%)
0: "__LABEL" CATEGORICAL integerized vocab-size:3 no-ood-item
Terminology:
nas: Number of non-available (i.e. missing) values.
ood: Out of dictionary.
manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
tokenized: The attribute value is obtained through tokenization.
has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
vocab-size: Number of unique values.
[INFO 24-04-20 11:17:36.9137 UTC kernel.cc:808] Configure learner
[INFO 24-04-20 11:17:36.9139 UTC kernel.cc:822] Training config:
learner: "RANDOM_FOREST"
features: "^sentence$"
label: "^__LABEL$"
task: CLASSIFICATION
random_seed: 123456
metadata {
framework: "TF Keras"
}
pure_serving_model: false
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
num_trees: 30
decision_tree {
max_depth: 16
min_examples: 5
in_split_min_examples_check: true
keep_non_leaf_label_distribution: true
num_candidate_attributes: 0
missing_value_policy: GLOBAL_IMPUTATION
allow_na_conditions: false
categorical_set_greedy_forward {
sampling: 0.1
max_num_items: -1
min_item_frequency: 1
}
growing_strategy_local {
}
categorical {
cart {
}
}
axis_aligned_split {
}
internal {
sorting_strategy: PRESORTED
}
uplift {
min_examples_in_treatment: 5
split_score: KULLBACK_LEIBLER
}
}
winner_take_all_inference: true
compute_oob_performances: true
compute_oob_variable_importances: false
num_oob_variable_importances_permutations: 1
bootstrap_training_dataset: true
bootstrap_size_ratio: 1
adapt_bootstrap_size_ratio_for_maximum_training_duration: false
sampling_with_replacement: true
}
[INFO 24-04-20 11:17:36.9143 UTC kernel.cc:825] Deployment config:
cache_path: "/tmpfs/tmp/tmpx28adgyq/working_cache"
num_threads: 32
try_resume_training: true
[INFO 24-04-20 11:17:36.9145 UTC kernel.cc:887] Train model
[INFO 24-04-20 11:17:36.9152 UTC random_forest.cc:416] Training random forest on 67349 example(s) and 1 feature(s).
[INFO 24-04-20 11:18:08.8767 UTC random_forest.cc:802] Training of tree 1/30 (tree index:1) done accuracy:0.7412 logloss:9.32811
[INFO 24-04-20 11:18:19.2026 UTC random_forest.cc:802] Training of tree 6/30 (tree index:27) done accuracy:0.775555 logloss:4.88012
[INFO 24-04-20 11:18:21.1285 UTC random_forest.cc:802] Training of tree 16/30 (tree index:25) done accuracy:0.808699 logloss:1.679
[INFO 24-04-20 11:18:22.6848 UTC random_forest.cc:802] Training of tree 26/30 (tree index:8) done accuracy:0.818557 logloss:0.904858
[INFO 24-04-20 11:18:24.0005 UTC random_forest.cc:802] Training of tree 30/30 (tree index:6) done accuracy:0.821274 logloss:0.854486
[INFO 24-04-20 11:18:24.0013 UTC random_forest.cc:882] Final OOB metrics: accuracy:0.821274 logloss:0.854486
[INFO 24-04-20 11:18:24.0104 UTC kernel.cc:919] Export model in log directory: /tmpfs/tmp/tmpx28adgyq with prefix da59c2f23fdb4012
[INFO 24-04-20 11:18:24.0388 UTC kernel.cc:937] Save model in resources
[INFO 24-04-20 11:18:24.0420 UTC abstract_model.cc:881] Model self evaluation:
Number of predictions (without weights): 67349
Number of predictions (with weights): 67349
Task: CLASSIFICATION
Label: __LABEL
Accuracy: 0.821274 CI95[W][0.818828 0.8237]
LogLoss: : 0.854486
ErrorRate: : 0.178726
Default Accuracy: : 0.557826
Default LogLoss: : 0.686445
Default ErrorRate: : 0.442174
Confusion Table:
truth\prediction
1 2
1 19593 10187
2 1850 35719
Total: 67349
[INFO 24-04-20 11:18:24.0658 UTC kernel.cc:1233] Loading model from path /tmpfs/tmp/tmpx28adgyq/model/ with prefix da59c2f23fdb4012
[INFO 24-04-20 11:18:24.3112 UTC decision_forest.cc:734] Model loaded with 30 root(s), 43180 node(s), and 1 input feature(s).
[INFO 24-04-20 11:18:24.3113 UTC abstract_model.cc:1344] Engine "RandomForestGeneric" built
[INFO 24-04-20 11:18:24.3113 UTC kernel.cc:1061] Use fast generic engine
Model trained in 0:00:47.515581
Compiling model...
Model compiled.
<tf_keras.src.callbacks.History at 0x7ff6107c01c0>
在先前的記錄中，請注意 sentence 是 CATEGORICAL_SET 特徵。
模型照常評估
model_1.compile(metrics=["accuracy"])
evaluation = model_1.evaluate(test_ds)
print(f"BinaryCrossentropyloss: {evaluation[0]}")
print(f"Accuracy: {evaluation[1]}")
9/9 [==============================] - 4s 4ms/step - loss: 0.0000e+00 - accuracy: 0.7638 BinaryCrossentropyloss: 0.0 Accuracy: 0.7637614607810974
訓練記錄如下所示
import matplotlib.pyplot as plt
logs = model_1.make_inspector().training_logs()
plt.plot([log.num_trees for log in logs], [log.evaluation.accuracy for log in logs])
plt.xlabel("Number of trees")
plt.ylabel("Out-of-bag accuracy")
pass
更多樹狀結構可能會更有益 (我很確定，因為我試過了 :p)。
使用預先訓練的文字嵌入
先前的範例使用原始文字特徵訓練隨機森林。此範例將使用預先訓練的 TF-Hub 嵌入將文字特徵轉換為密集嵌入，然後在其之上訓練隨機森林。在這種情況下，隨機森林只會「看到」嵌入的數值輸出 (即，它不會看到原始文字)。
在此實驗中，將使用 Universal-Sentence-Encoder。不同的預先訓練嵌入可能適合不同類型的文字 (例如，不同的語言、不同的任務)，但也適合其他類型的結構化特徵 (例如，圖片)。
嵌入模組可以在兩個位置之一套用
- 在資料集準備期間。
- 在模型的預先處理階段。
第二個選項通常更佳：將嵌入封裝在模型中使模型更易於使用 (且更難誤用)。
首先安裝 TF-Hub
pip install --upgrade tensorflow-hub
與之前不同，您不需要對文字進行符號化。
def prepare_dataset(example):
label = (example["label"] + 1) // 2
return {"sentence" : example["sentence"]}, label
train_ds = all_ds["train"].batch(100).map(prepare_dataset)
test_ds = all_ds["validation"].batch(100).map(prepare_dataset)
%set_cell_height 300
import tensorflow_hub as hub
# NNLM (https://tfhub.dev/google/nnlm-en-dim128/2) is also a good choice.
hub_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
embedding = hub.KerasLayer(hub_url)
sentence = tf_keras.layers.Input(shape=(), name="sentence", dtype=tf.string)
embedded_sentence = embedding(sentence)
raw_inputs = {"sentence": sentence}
processed_inputs = {"embedded_sentence": embedded_sentence}
preprocessor = tf_keras.Model(inputs=raw_inputs, outputs=processed_inputs)
model_2 = tfdf.keras.RandomForestModel(
preprocessing=preprocessor,
num_trees=100)
model_2.fit(x=train_ds)
<IPython.core.display.Javascript object> Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus. WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus. Use /tmpfs/tmp/tmpv_kuhy0b as temporary training directory Reading training dataset... Training dataset read in 0:00:24.071412. Found 67349 examples. Training model... [INFO 24-04-20 11:19:25.9064 UTC kernel.cc:1233] Loading model from path /tmpfs/tmp/tmpv_kuhy0b/model/ with prefix 36a4a9d3f10743e4 Model trained in 0:00:13.926431 Compiling model... [INFO 24-04-20 11:19:27.6042 UTC decision_forest.cc:734] Model loaded with 100 root(s), 565608 node(s), and 512 input feature(s). [INFO 24-04-20 11:19:27.6044 UTC abstract_model.cc:1344] Engine "RandomForestOptPred" built [INFO 24-04-20 11:19:27.6045 UTC kernel.cc:1061] Use fast generic engine Model compiled. <tf_keras.src.callbacks.History at 0x7ff4202c16d0>
model_2.compile(metrics=["accuracy"])
evaluation = model_2.evaluate(test_ds)
print(f"BinaryCrossentropyloss: {evaluation[0]}")
print(f"Accuracy: {evaluation[1]}")
9/9 [==============================] - 2s 18ms/step - loss: 0.0000e+00 - accuracy: 0.7878 BinaryCrossentropyloss: 0.0 Accuracy: 0.7878440618515015
請注意，類別集合以不同於密集嵌入的方式表示文字，因此同時使用這兩種策略可能很有用。
一起訓練決策樹和神經網路
先前的範例使用預先訓練的神經網路 (NN) 在將文字特徵傳遞到隨機森林之前處理它們。此範例將從頭開始訓練神經網路和隨機森林。
TF-DF 的決策森林不會反向傳播梯度 (儘管這是正在進行研究的主題)。因此，訓練分兩個階段進行
- 將神經網路訓練為標準分類任務
example → [Normalize] → [Neural Network*] → [classification head] → prediction
*: Training.
- 將神經網路的標頭 (最後一層和 softmax) 替換為隨機森林。照常訓練隨機森林
example → [Normalize] → [Neural Network] → [Random Forest*] → prediction
*: Training.
準備資料集
此範例使用帕默企鵝資料集。如需詳細資訊，請參閱初階 Colab。
首先，下載原始資料
wget -q https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv -O /tmp/penguins.csv
將資料集載入 Pandas DataFrame。
dataset_df = pd.read_csv("/tmp/penguins.csv")
# Display the first 3 examples.
dataset_df.head(3)
準備用於訓練的資料集。
label = "species"
# Replaces numerical NaN (representing missing values in Pandas Dataframe) with 0s.
# ...Neural Nets don't work well with numerical NaNs.
for col in dataset_df.columns:
if dataset_df[col].dtype not in [str, object]:
dataset_df[col] = dataset_df[col].fillna(0)
# Split the dataset into a training and testing dataset.
def split_dataset(dataset, test_ratio=0.30):
"""Splits a panda dataframe in two."""
test_indices = np.random.rand(len(dataset)) < test_ratio
return dataset[~test_indices], dataset[test_indices]
train_ds_pd, test_ds_pd = split_dataset(dataset_df)
print("{} examples in training, {} examples for testing.".format(
len(train_ds_pd), len(test_ds_pd)))
# Convert the datasets into tensorflow datasets
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label)
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label)
248 examples in training, 96 examples for testing.
建構模型
接下來，使用 Keras 的函數式樣式建立神經網路模型。
為了使範例保持簡單，此模型僅使用兩個輸入。
input_1 = tf_keras.Input(shape=(1,), name="bill_length_mm", dtype="float")
input_2 = tf_keras.Input(shape=(1,), name="island", dtype="string")
nn_raw_inputs = [input_1, input_2]
使用預先處理層將原始輸入轉換為適用於神經網路的輸入。
# Normalization.
Normalization = tf_keras.layers.Normalization
CategoryEncoding = tf_keras.layers.CategoryEncoding
StringLookup = tf_keras.layers.StringLookup
values = train_ds_pd["bill_length_mm"].values[:, tf.newaxis]
input_1_normalizer = Normalization()
input_1_normalizer.adapt(values)
values = train_ds_pd["island"].values
input_2_indexer = StringLookup(max_tokens=32)
input_2_indexer.adapt(values)
input_2_onehot = CategoryEncoding(output_mode="binary", max_tokens=32)
normalized_input_1 = input_1_normalizer(input_1)
normalized_input_2 = input_2_onehot(input_2_indexer(input_2))
nn_processed_inputs = [normalized_input_1, normalized_input_2]
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead. WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
建構神經網路的主體
y = tf_keras.layers.Concatenate()(nn_processed_inputs)
y = tf_keras.layers.Dense(16, activation=tf.nn.relu6)(y)
last_layer = tf_keras.layers.Dense(8, activation=tf.nn.relu, name="last")(y)
# "3" for the three label classes. If it were a binary classification, the
# output dim would be 1.
classification_output = tf_keras.layers.Dense(3)(y)
nn_model = tf_keras.models.Model(nn_raw_inputs, classification_output)
此 nn_model 直接產生分類 logits。
接下來，建立決策森林模型。這將在神經網路在分類標頭之前的最後一層中提取的高階特徵上運作。
# To reduce the risk of mistakes, group both the decision forest and the
# neural network in a single keras model.
nn_without_head = tf_keras.models.Model(inputs=nn_model.inputs, outputs=last_layer)
df_and_nn_model = tfdf.keras.RandomForestModel(preprocessing=nn_without_head)
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus. WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus. Use /tmpfs/tmp/tmpgsdeyzk5 as temporary training directory
訓練和評估模型
模型將分兩個階段進行訓練。首先訓練具有自身分類標頭的神經網路
%set_cell_height 300
nn_model.compile(
optimizer=tf_keras.optimizers.Adam(),
loss=tf_keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=["accuracy"])
nn_model.fit(x=train_ds, validation_data=test_ds, epochs=10)
nn_model.summary()
<IPython.core.display.Javascript object>
Epoch 1/10
/tmpfs/tmp/__autograph_generated_filetvdtpwer.py:63: UserWarning: Input dict contained keys ['bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex', 'year'] which did not match any model input. They will be ignored by the model.
ag__.converted_call(ag__.ld(warnings).warn, (ag__.converted_call('Input dict contained keys {} which did not match any model input. They will be ignored by the model.'.format, ([ag__.ld(n) for n in ag__.converted_call(ag__.ld(tensors).keys, (), None, fscope) if ag__.ld(n) not in ag__.ld(ref_input_names)],), None, fscope),), dict(stacklevel=2), fscope)
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1713611983.477059 22965 service.cc:145] XLA service 0x7ff3c4155d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1713611983.477103 22965 service.cc:153] StreamExecutor device (0): Tesla T4, Compute Capability 7.5
I0000 00:00:1713611983.477109 22965 service.cc:153] StreamExecutor device (1): Tesla T4, Compute Capability 7.5
I0000 00:00:1713611983.477112 22965 service.cc:153] StreamExecutor device (2): Tesla T4, Compute Capability 7.5
I0000 00:00:1713611983.477115 22965 service.cc:153] StreamExecutor device (3): Tesla T4, Compute Capability 7.5
I0000 00:00:1713611983.604064 22965 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
1/1 [==============================] - 7s 7s/step - loss: 1.0737 - accuracy: 0.7137 - val_loss: 1.0569 - val_accuracy: 0.7500
Epoch 2/10
1/1 [==============================] - 0s 24ms/step - loss: 1.0696 - accuracy: 0.7177 - val_loss: 1.0529 - val_accuracy: 0.7500
Epoch 3/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0655 - accuracy: 0.7177 - val_loss: 1.0489 - val_accuracy: 0.7500
Epoch 4/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0614 - accuracy: 0.7218 - val_loss: 1.0450 - val_accuracy: 0.7500
Epoch 5/10
1/1 [==============================] - 0s 23ms/step - loss: 1.0574 - accuracy: 0.7258 - val_loss: 1.0410 - val_accuracy: 0.7500
Epoch 6/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0533 - accuracy: 0.7298 - val_loss: 1.0371 - val_accuracy: 0.7708
Epoch 7/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0494 - accuracy: 0.7339 - val_loss: 1.0332 - val_accuracy: 0.7708
Epoch 8/10
1/1 [==============================] - 0s 25ms/step - loss: 1.0454 - accuracy: 0.7379 - val_loss: 1.0293 - val_accuracy: 0.7708
Epoch 9/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0415 - accuracy: 0.7419 - val_loss: 1.0254 - val_accuracy: 0.7812
Epoch 10/10
1/1 [==============================] - 0s 22ms/step - loss: 1.0376 - accuracy: 0.7460 - val_loss: 1.0217 - val_accuracy: 0.7812
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
island (InputLayer) [(None, 1)] 0 []
bill_length_mm (InputLayer [(None, 1)] 0 []
)
string_lookup (StringLooku (None, 1) 0 ['island[0][0]']
p)
normalization (Normalizati (None, 1) 3 ['bill_length_mm[0][0]']
on)
category_encoding (Categor (None, 32) 0 ['string_lookup[0][0]']
yEncoding)
concatenate (Concatenate) (None, 33) 0 ['normalization[0][0]',
'category_encoding[0][0]']
dense (Dense) (None, 16) 544 ['concatenate[0][0]']
dense_1 (Dense) (None, 3) 51 ['dense[0][0]']
==================================================================================================
Total params: 598 (2.34 KB)
Trainable params: 595 (2.32 KB)
Non-trainable params: 3 (16.00 Byte)
__________________________________________________________________________________________________
神經網路層在兩個模型之間共用。因此，現在神經網路已訓練完成，決策森林模型將適合神經網路層的已訓練輸出
%set_cell_height 300
df_and_nn_model.fit(x=train_ds)
<IPython.core.display.Javascript object> Reading training dataset... Training dataset read in 0:00:00.463558. Found 248 examples. Training model... Model trained in 0:00:00.043113 Compiling model... [INFO 24-04-20 11:19:45.0975 UTC kernel.cc:1233] Loading model from path /tmpfs/tmp/tmpgsdeyzk5/model/ with prefix ac3d07af419249e2 [INFO 24-04-20 11:19:45.1138 UTC decision_forest.cc:734] Model loaded with 300 root(s), 5640 node(s), and 8 input feature(s). [INFO 24-04-20 11:19:45.1138 UTC abstract_model.cc:1344] Engine "RandomForestGeneric" built [INFO 24-04-20 11:19:45.1138 UTC kernel.cc:1061] Use fast generic engine Model compiled. <tf_keras.src.callbacks.History at 0x7ff3fc7e2ac0>
現在評估組合模型
df_and_nn_model.compile(metrics=["accuracy"])
print("Evaluation:", df_and_nn_model.evaluate(test_ds))
1/1 [==============================] - 0s 240ms/step - loss: 0.0000e+00 - accuracy: 0.9479 Evaluation: [0.0, 0.9479166865348816]
將其與單獨的神經網路進行比較
print("Evaluation :", nn_model.evaluate(test_ds))
1/1 [==============================] - 0s 14ms/step - loss: 1.0217 - accuracy: 0.7812 Evaluation : [1.021651029586792, 0.78125]