Keras 2 : examples : コンピュータビジョン – CAPTCHA を読むための OCR モデル (翻訳/解説)
翻訳 : (株)クラスキャット セールスインフォメーション
作成日時 : 11/05/2021 (keras 2.6.0)
* 本ページは、Keras の以下のドキュメントを翻訳した上で適宜、補足説明したものです：
- Code examples : Computer Vision : OCR model for reading Captchas (Author: A_K_Nain)
* サンプルコードの動作確認はしておりますが、必要な場合には適宜、追加改変しています。
* ご自由にリンクを張って頂いてかまいませんが、sales-info@classcat.com までご一報いただけると嬉しいです。
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Keras 2 : examples : CAPTCHA を読むための OCR モデル
イントロダクション
このサンプルは関数型 API で構築された単純な OCR モデルを実演します。CNN と RNN の組合せ以外にも、それは新しい層をインスタンス化してそれを CTC 損失の実装のための “Endpoint 層” として使用できる方法も示します。層のサブクラス化への詳細なガイドは、開発者ガイドの このページ を確認してください。
セットアップ
import os
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from collections import Counter
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
データ: Captcha 画像をロードする
データをダウンロードしましょう。
!curl -LO https://github.com/AakashKumarNain/CaptchaCracker/raw/master/captcha_images_v2.zip
!unzip -qq captcha_images_v2.zip
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 159 100 159 0 0 164 0 --:--:-- --:--:-- --:--:-- 164
100 8863k 100 8863k 0 0 4882k 0 0:00:01 0:00:01 --:--:-- 33.0M
データセットは png 画像として 1040 captcha ファイルを含みます。各サンプルのためのラベルは文字列、ファイル名です (ファイル拡張子を引いたもの)。モデルを訓練するために文字列の各文字を整数にマップします。同様に、モデルの予測を文字列にマップし戻す必要があります。この目的で 2 つの辞書を維持します、それぞれ文字を整数に、整数を文字にマップします。
# Path to the data directory
data_dir = Path("./captcha_images_v2/")
# Get list of all the images
images = sorted(list(map(str, list(data_dir.glob("*.png")))))
labels = [img.split(os.path.sep)[-1].split(".png")[0] for img in images]
characters = set(char for label in labels for char in label)
print("Number of images found: ", len(images))
print("Number of labels found: ", len(labels))
print("Number of unique characters: ", len(characters))
print("Characters present: ", characters)
# Batch size for training and validation
batch_size = 16
# Desired image dimensions
img_width = 200
img_height = 50
# Factor by which the image is going to be downsampled
# by the convolutional blocks. We will be using two
# convolution blocks and each block will have
# a pooling layer which downsample the features by a factor of 2.
# Hence total downsampling factor would be 4.
downsample_factor = 4
# Maximum length of any captcha in the dataset
max_length = max([len(label) for label in labels])
Number of images found: 1040
Number of labels found: 1040
Number of unique characters: 19
Characters present: {'d', 'w', 'y', '4', 'f', '6', 'g', 'e', '3', '5', 'p', 'x', '2', 'c', '7', 'n', 'b', '8', 'm'}
前処理
# Mapping characters to integers
char_to_num = layers.StringLookup(
vocabulary=list(characters), mask_token=None
)
# Mapping integers back to original characters
num_to_char = layers.StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
def split_data(images, labels, train_size=0.9, shuffle=True):
# 1. Get the total size of the dataset
size = len(images)
# 2. Make an indices array and shuffle it, if required
indices = np.arange(size)
if shuffle:
np.random.shuffle(indices)
# 3. Get the size of training samples
train_samples = int(size * train_size)
# 4. Split data into training and validation sets
x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]
x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]
return x_train, x_valid, y_train, y_valid
# Splitting data into training and validation sets
x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels))
def encode_single_sample(img_path, label):
# 1. Read image
img = tf.io.read_file(img_path)
# 2. Decode and convert to grayscale
img = tf.io.decode_png(img, channels=1)
# 3. Convert to float32 in [0, 1] range
img = tf.image.convert_image_dtype(img, tf.float32)
# 4. Resize to the desired size
img = tf.image.resize(img, [img_height, img_width])
# 5. Transpose the image because we want the time
# dimension to correspond to the width of the image.
img = tf.transpose(img, perm=[1, 0, 2])
# 6. Map the characters in label to numbers
label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
# 7. Return a dict as our model is expecting two inputs
return {"image": img, "label": label}
データセット・オブジェクトの作成
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))
validation_dataset = (
validation_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
データの可視化
_, ax = plt.subplots(4, 4, figsize=(10, 5))
for batch in train_dataset.take(1):
images = batch["image"]
labels = batch["label"]
for i in range(16):
img = (images[i] * 255).numpy().astype("uint8")
label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode("utf-8")
ax[i // 4, i % 4].imshow(img[:, :, 0].T, cmap="gray")
ax[i // 4, i % 4].set_title(label)
ax[i // 4, i % 4].axis("off")
plt.show()
モデル
class CTCLayer(layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = keras.backend.ctc_batch_cost
def call(self, y_true, y_pred):
# Compute the training-time loss value and add it
# to the layer using `self.add_loss()`.
batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64")
input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64")
label_length = tf.cast(tf.shape(y_true)[1], dtype="int64")
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64")
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64")
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# At test time, just return the computed predictions
return y_pred
def build_model():
# Inputs to the model
input_img = layers.Input(
shape=(img_width, img_height, 1), name="image", dtype="float32"
)
labels = layers.Input(name="label", shape=(None,), dtype="float32")
# First conv block
x = layers.Conv2D(
32,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv1",
)(input_img)
x = layers.MaxPooling2D((2, 2), name="pool1")(x)
# Second conv block
x = layers.Conv2D(
64,
(3, 3),
activation="relu",
kernel_initializer="he_normal",
padding="same",
name="Conv2",
)(x)
x = layers.MaxPooling2D((2, 2), name="pool2")(x)
# We have used two max pool with pool size and strides 2.
# Hence, downsampled feature maps are 4x smaller. The number of
# filters in the last layer is 64. Reshape accordingly before
# passing the output to the RNN part of the model
new_shape = ((img_width // 4), (img_height // 4) * 64)
x = layers.Reshape(target_shape=new_shape, name="reshape")(x)
x = layers.Dense(64, activation="relu", name="dense1")(x)
x = layers.Dropout(0.2)(x)
# RNNs
x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)
x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)
# Output layer
x = layers.Dense(
len(char_to_num.get_vocabulary()) + 1, activation="softmax", name="dense2"
)(x)
# Add CTC layer for calculating CTC loss at each step
output = CTCLayer(name="ctc_loss")(labels, x)
# Define the model
model = keras.models.Model(
inputs=[input_img, labels], outputs=output, name="ocr_model_v1"
)
# Optimizer
opt = keras.optimizers.Adam()
# Compile the model and return
model.compile(optimizer=opt)
return model
# Get the model
model = build_model()
model.summary()
Model: "ocr_model_v1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
image (InputLayer) [(None, 200, 50, 1)] 0
__________________________________________________________________________________________________
Conv1 (Conv2D) (None, 200, 50, 32) 320 image[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 100, 25, 32) 0 Conv1[0][0]
__________________________________________________________________________________________________
Conv2 (Conv2D) (None, 100, 25, 64) 18496 pool1[0][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None, 50, 12, 64) 0 Conv2[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, 50, 768) 0 pool2[0][0]
__________________________________________________________________________________________________
dense1 (Dense) (None, 50, 64) 49216 reshape[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 50, 64) 0 dense1[0][0]
__________________________________________________________________________________________________
bidirectional (Bidirectional) (None, 50, 256) 197632 dropout[0][0]
__________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 50, 128) 164352 bidirectional[0][0]
__________________________________________________________________________________________________
label (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
dense2 (Dense) (None, 50, 20) 2580 bidirectional_1[0][0]
__________________________________________________________________________________________________
ctc_loss (CTCLayer) (None, 50, 20) 0 label[0][0]
dense2[0][0]
==================================================================================================
Total params: 432,596
Trainable params: 432,596
Non-trainable params: 0
訓練
epochs = 100
early_stopping_patience = 10
# Add early stopping
early_stopping = keras.callbacks.EarlyStopping(
monitor="val_loss", patience=early_stopping_patience, restore_best_weights=True
)
# Train the model
history = model.fit(
train_dataset,
validation_data=validation_dataset,
epochs=epochs,
callbacks=[early_stopping],
)
Epoch 1/100 59/59 [==============================] - 3s 53ms/step - loss: 21.5722 - val_loss: 16.3351 Epoch 2/100 59/59 [==============================] - 2s 27ms/step - loss: 16.3335 - val_loss: 16.3062 Epoch 3/100 59/59 [==============================] - 2s 27ms/step - loss: 16.3360 - val_loss: 16.3116 Epoch 4/100 59/59 [==============================] - 2s 27ms/step - loss: 16.3318 - val_loss: 16.3167 Epoch 5/100 59/59 [==============================] - 2s 27ms/step - loss: 16.3256 - val_loss: 16.3152 Epoch 6/100 59/59 [==============================] - 2s 29ms/step - loss: 16.3229 - val_loss: 16.3123 Epoch 7/100 59/59 [==============================] - 2s 30ms/step - loss: 16.3119 - val_loss: 16.3116 Epoch 8/100 59/59 [==============================] - 2s 27ms/step - loss: 16.2977 - val_loss: 16.3107 Epoch 9/100 59/59 [==============================] - 2s 28ms/step - loss: 16.2801 - val_loss: 16.2552 Epoch 10/100 59/59 [==============================] - 2s 28ms/step - loss: 16.2199 - val_loss: 16.1008 Epoch 11/100 59/59 [==============================] - 2s 28ms/step - loss: 16.1136 - val_loss: 15.9867 Epoch 12/100 59/59 [==============================] - 2s 30ms/step - loss: 16.0138 - val_loss: 15.8825 Epoch 13/100 59/59 [==============================] - 2s 29ms/step - loss: 15.9670 - val_loss: 15.8413 Epoch 14/100 59/59 [==============================] - 2s 29ms/step - loss: 15.9315 - val_loss: 15.8263 Epoch 15/100 59/59 [==============================] - 2s 31ms/step - loss: 15.9162 - val_loss: 15.7971 Epoch 16/100 59/59 [==============================] - 2s 31ms/step - loss: 15.8916 - val_loss: 15.7844 Epoch 17/100 59/59 [==============================] - 2s 31ms/step - loss: 15.8653 - val_loss: 15.7624 Epoch 18/100 59/59 [==============================] - 2s 31ms/step - loss: 15.8543 - val_loss: 15.7620 Epoch 19/100 59/59 [==============================] - 2s 28ms/step - loss: 15.8373 - val_loss: 15.7559 Epoch 20/100 59/59 [==============================] - 2s 27ms/step - loss: 15.8319 - val_loss: 15.7495 Epoch 21/100 59/59 [==============================] - 2s 27ms/step - loss: 15.8104 - val_loss: 15.7430 Epoch 22/100 59/59 [==============================] - 2s 29ms/step - loss: 15.8037 - val_loss: 15.7260 Epoch 23/100 59/59 [==============================] - 2s 29ms/step - loss: 15.8021 - val_loss: 15.7204 Epoch 24/100 59/59 [==============================] - 2s 28ms/step - loss: 15.7901 - val_loss: 15.7174 Epoch 25/100 59/59 [==============================] - 2s 29ms/step - loss: 15.7851 - val_loss: 15.7074 Epoch 26/100 59/59 [==============================] - 2s 27ms/step - loss: 15.7701 - val_loss: 15.7097 Epoch 27/100 59/59 [==============================] - 2s 28ms/step - loss: 15.7694 - val_loss: 15.7040 Epoch 28/100 59/59 [==============================] - 2s 28ms/step - loss: 15.7544 - val_loss: 15.7012 Epoch 29/100 59/59 [==============================] - 2s 31ms/step - loss: 15.7498 - val_loss: 15.7015 Epoch 30/100 59/59 [==============================] - 2s 31ms/step - loss: 15.7521 - val_loss: 15.6880 Epoch 31/100 59/59 [==============================] - 2s 29ms/step - loss: 15.7165 - val_loss: 15.6734 Epoch 32/100 59/59 [==============================] - 2s 27ms/step - loss: 15.6650 - val_loss: 15.5789 Epoch 33/100 59/59 [==============================] - 2s 27ms/step - loss: 15.5300 - val_loss: 15.4026 Epoch 34/100 59/59 [==============================] - 2s 27ms/step - loss: 15.3519 - val_loss: 15.2115 Epoch 35/100 59/59 [==============================] - 2s 27ms/step - loss: 15.1165 - val_loss: 14.7826 Epoch 36/100 59/59 [==============================] - 2s 27ms/step - loss: 14.7086 - val_loss: 14.4432 Epoch 37/100 59/59 [==============================] - 2s 29ms/step - loss: 14.3317 - val_loss: 13.9445 Epoch 38/100 59/59 [==============================] - 2s 29ms/step - loss: 13.9658 - val_loss: 13.6972 Epoch 39/100 59/59 [==============================] - 2s 29ms/step - loss: 13.6728 - val_loss: 13.3388 Epoch 40/100 59/59 [==============================] - 2s 28ms/step - loss: 13.3454 - val_loss: 13.0102 Epoch 41/100 59/59 [==============================] - 2s 27ms/step - loss: 13.0448 - val_loss: 12.8307 Epoch 42/100 59/59 [==============================] - 2s 28ms/step - loss: 12.7552 - val_loss: 12.6071 Epoch 43/100 59/59 [==============================] - 2s 29ms/step - loss: 12.4573 - val_loss: 12.2800 Epoch 44/100 59/59 [==============================] - 2s 31ms/step - loss: 12.1055 - val_loss: 11.9209 Epoch 45/100 59/59 [==============================] - 2s 28ms/step - loss: 11.8148 - val_loss: 11.9132 Epoch 46/100 59/59 [==============================] - 2s 28ms/step - loss: 11.4530 - val_loss: 11.4357 Epoch 47/100 59/59 [==============================] - 2s 29ms/step - loss: 11.0592 - val_loss: 11.1121 Epoch 48/100 59/59 [==============================] - 2s 27ms/step - loss: 10.7746 - val_loss: 10.8532 Epoch 49/100 59/59 [==============================] - 2s 28ms/step - loss: 10.2616 - val_loss: 10.3643 Epoch 50/100 59/59 [==============================] - 2s 28ms/step - loss: 9.8708 - val_loss: 10.0987 Epoch 51/100 59/59 [==============================] - 2s 30ms/step - loss: 9.4077 - val_loss: 9.6371 Epoch 52/100 59/59 [==============================] - 2s 29ms/step - loss: 9.0663 - val_loss: 9.2463 Epoch 53/100 59/59 [==============================] - 2s 28ms/step - loss: 8.4546 - val_loss: 8.7581 Epoch 54/100 59/59 [==============================] - 2s 28ms/step - loss: 7.9226 - val_loss: 8.1805 Epoch 55/100 59/59 [==============================] - 2s 27ms/step - loss: 7.4927 - val_loss: 7.8858 Epoch 56/100 59/59 [==============================] - 2s 28ms/step - loss: 7.0499 - val_loss: 7.3202 Epoch 57/100 59/59 [==============================] - 2s 27ms/step - loss: 6.6383 - val_loss: 7.0875 Epoch 58/100 59/59 [==============================] - 2s 28ms/step - loss: 6.1446 - val_loss: 6.9619 Epoch 59/100 59/59 [==============================] - 2s 28ms/step - loss: 5.8533 - val_loss: 6.3855 Epoch 60/100 59/59 [==============================] - 2s 28ms/step - loss: 5.5107 - val_loss: 5.9797 Epoch 61/100 59/59 [==============================] - 2s 31ms/step - loss: 5.1181 - val_loss: 5.7549 Epoch 62/100 59/59 [==============================] - 2s 31ms/step - loss: 4.6952 - val_loss: 5.5488 Epoch 63/100 59/59 [==============================] - 2s 29ms/step - loss: 4.4189 - val_loss: 5.3030 Epoch 64/100 59/59 [==============================] - 2s 28ms/step - loss: 4.1358 - val_loss: 5.1772 Epoch 65/100 59/59 [==============================] - 2s 28ms/step - loss: 3.8560 - val_loss: 5.1071 Epoch 66/100 59/59 [==============================] - 2s 28ms/step - loss: 3.5342 - val_loss: 4.6958 Epoch 67/100 59/59 [==============================] - 2s 28ms/step - loss: 3.3336 - val_loss: 4.5865 Epoch 68/100 59/59 [==============================] - 2s 27ms/step - loss: 3.0925 - val_loss: 4.3647 Epoch 69/100 59/59 [==============================] - 2s 28ms/step - loss: 2.8751 - val_loss: 4.3005 Epoch 70/100 59/59 [==============================] - 2s 27ms/step - loss: 2.7444 - val_loss: 4.0820 Epoch 71/100 59/59 [==============================] - 2s 27ms/step - loss: 2.5921 - val_loss: 4.1694 Epoch 72/100 59/59 [==============================] - 2s 28ms/step - loss: 2.3246 - val_loss: 3.9142 Epoch 73/100 59/59 [==============================] - 2s 28ms/step - loss: 2.0769 - val_loss: 3.9135 Epoch 74/100 59/59 [==============================] - 2s 29ms/step - loss: 2.0872 - val_loss: 3.9808 Epoch 75/100 59/59 [==============================] - 2s 29ms/step - loss: 1.9498 - val_loss: 3.9935 Epoch 76/100 59/59 [==============================] - 2s 28ms/step - loss: 1.8178 - val_loss: 3.7735 Epoch 77/100 59/59 [==============================] - 2s 29ms/step - loss: 1.7661 - val_loss: 3.6309 Epoch 78/100 59/59 [==============================] - 2s 31ms/step - loss: 1.6236 - val_loss: 3.7410 Epoch 79/100 59/59 [==============================] - 2s 29ms/step - loss: 1.4652 - val_loss: 3.6756 Epoch 80/100 59/59 [==============================] - 2s 27ms/step - loss: 1.3552 - val_loss: 3.4979 Epoch 81/100 59/59 [==============================] - 2s 29ms/step - loss: 1.2655 - val_loss: 3.5306 Epoch 82/100 59/59 [==============================] - 2s 29ms/step - loss: 1.2632 - val_loss: 3.2885 Epoch 83/100 59/59 [==============================] - 2s 28ms/step - loss: 1.2316 - val_loss: 3.2482 Epoch 84/100 59/59 [==============================] - 2s 30ms/step - loss: 1.1260 - val_loss: 3.4285 Epoch 85/100 59/59 [==============================] - 2s 28ms/step - loss: 1.0745 - val_loss: 3.2985 Epoch 86/100 59/59 [==============================] - 2s 29ms/step - loss: 1.0133 - val_loss: 3.2209 Epoch 87/100 59/59 [==============================] - 2s 31ms/step - loss: 0.9417 - val_loss: 3.2203 Epoch 88/100 59/59 [==============================] - 2s 28ms/step - loss: 0.9104 - val_loss: 3.1121 Epoch 89/100 59/59 [==============================] - 2s 30ms/step - loss: 0.8516 - val_loss: 3.2070 Epoch 90/100 59/59 [==============================] - 2s 28ms/step - loss: 0.8275 - val_loss: 3.0335 Epoch 91/100 59/59 [==============================] - 2s 28ms/step - loss: 0.8056 - val_loss: 3.2085 Epoch 92/100 59/59 [==============================] - 2s 28ms/step - loss: 0.7373 - val_loss: 3.0326 Epoch 93/100 59/59 [==============================] - 2s 28ms/step - loss: 0.7753 - val_loss: 2.9935 Epoch 94/100 59/59 [==============================] - 2s 28ms/step - loss: 0.7688 - val_loss: 2.9940 Epoch 95/100 59/59 [==============================] - 2s 27ms/step - loss: 0.6765 - val_loss: 3.0432 Epoch 96/100 59/59 [==============================] - 2s 29ms/step - loss: 0.6674 - val_loss: 3.1233 Epoch 97/100 59/59 [==============================] - 2s 29ms/step - loss: 0.6018 - val_loss: 2.8405 Epoch 98/100 59/59 [==============================] - 2s 28ms/step - loss: 0.6322 - val_loss: 2.8323 Epoch 99/100 59/59 [==============================] - 2s 29ms/step - loss: 0.5889 - val_loss: 2.8786 Epoch 100/100 59/59 [==============================] - 2s 28ms/step - loss: 0.5616 - val_loss: 2.9697
(訳注: 実験結果)
Epoch 1/100 59/59 [==============================] - 24s 56ms/step - loss: 21.5466 - val_loss: 16.4842 Epoch 2/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3485 - val_loss: 16.4333 Epoch 3/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3448 - val_loss: 16.4257 Epoch 4/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3370 - val_loss: 16.4155 Epoch 5/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3331 - val_loss: 16.4079 Epoch 6/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3276 - val_loss: 16.4134 Epoch 7/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3218 - val_loss: 16.4159 Epoch 8/100 59/59 [==============================] - 2s 33ms/step - loss: 16.3176 - val_loss: 16.4182 Epoch 9/100 59/59 [==============================] - 2s 32ms/step - loss: 16.3039 - val_loss: 16.4151 Epoch 10/100 59/59 [==============================] - 2s 32ms/step - loss: 16.2702 - val_loss: 16.3513 Epoch 11/100 59/59 [==============================] - 2s 33ms/step - loss: 16.0892 - val_loss: 16.0135 Epoch 12/100 59/59 [==============================] - 2s 34ms/step - loss: 15.4134 - val_loss: 14.7452 Epoch 13/100 59/59 [==============================] - 2s 33ms/step - loss: 13.0894 - val_loss: 10.4998 Epoch 14/100 59/59 [==============================] - 2s 33ms/step - loss: 8.2140 - val_loss: 4.6543 Epoch 15/100 59/59 [==============================] - 2s 33ms/step - loss: 3.7345 - val_loss: 1.8389 Epoch 16/100 59/59 [==============================] - 2s 32ms/step - loss: 2.1812 - val_loss: 1.3311 Epoch 17/100 59/59 [==============================] - 2s 32ms/step - loss: 1.5639 - val_loss: 0.9540 Epoch 18/100 59/59 [==============================] - 2s 33ms/step - loss: 1.1425 - val_loss: 0.5993 Epoch 19/100 59/59 [==============================] - 2s 33ms/step - loss: 0.7529 - val_loss: 0.3300 Epoch 20/100 59/59 [==============================] - 2s 33ms/step - loss: 0.5688 - val_loss: 0.2294 Epoch 21/100 59/59 [==============================] - 2s 32ms/step - loss: 0.2925 - val_loss: 0.1670 Epoch 22/100 59/59 [==============================] - 2s 33ms/step - loss: 0.2348 - val_loss: 0.1984 Epoch 23/100 59/59 [==============================] - 2s 32ms/step - loss: 0.2218 - val_loss: 0.1594 Epoch 24/100 59/59 [==============================] - 2s 32ms/step - loss: 0.1871 - val_loss: 0.1384 Epoch 25/100 59/59 [==============================] - 2s 33ms/step - loss: 0.1473 - val_loss: 0.1467 Epoch 26/100 59/59 [==============================] - 2s 32ms/step - loss: 0.1721 - val_loss: 0.1424 Epoch 27/100 59/59 [==============================] - 2s 33ms/step - loss: 0.1246 - val_loss: 0.1266 Epoch 28/100 59/59 [==============================] - 2s 33ms/step - loss: 0.1328 - val_loss: 0.1314 Epoch 29/100 59/59 [==============================] - 2s 34ms/step - loss: 0.1025 - val_loss: 0.1357 Epoch 30/100 59/59 [==============================] - 2s 34ms/step - loss: 0.1284 - val_loss: 0.1441 Epoch 31/100 59/59 [==============================] - 2s 35ms/step - loss: 0.1144 - val_loss: 0.1341 Epoch 32/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0799 - val_loss: 0.1521 Epoch 33/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0647 - val_loss: 0.1167 Epoch 34/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0603 - val_loss: 0.1477 Epoch 35/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0674 - val_loss: 0.1389 Epoch 36/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0875 - val_loss: 0.1506 Epoch 37/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0759 - val_loss: 0.1468 Epoch 38/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0464 - val_loss: 0.1259 Epoch 39/100 59/59 [==============================] - 2s 33ms/step - loss: 0.1236 - val_loss: 0.1157 Epoch 40/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0852 - val_loss: 0.0998 Epoch 41/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0621 - val_loss: 0.1050 Epoch 42/100 59/59 [==============================] - 2s 32ms/step - loss: 0.0928 - val_loss: 0.1574 Epoch 43/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0514 - val_loss: 0.1766 Epoch 44/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0409 - val_loss: 0.1554 Epoch 45/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0394 - val_loss: 0.1557 Epoch 46/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0592 - val_loss: 0.1569 Epoch 47/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0352 - val_loss: 0.1584 Epoch 48/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0266 - val_loss: 0.1418 Epoch 49/100 59/59 [==============================] - 2s 34ms/step - loss: 0.0731 - val_loss: 0.1778 Epoch 50/100 59/59 [==============================] - 2s 33ms/step - loss: 0.0521 - val_loss: 0.1646 CPU times: user 2min 25s, sys: 16.3 s, total: 2min 41s Wall time: 2min 14s
推論
# Get the prediction model by extracting layers till the output layer
prediction_model = keras.models.Model(
model.get_layer(name="image").input, model.get_layer(name="dense2").output
)
prediction_model.summary()
# A utility function to decode the output of the network
def decode_batch_predictions(pred):
input_len = np.ones(pred.shape[0]) * pred.shape[1]
# Use greedy search. For complex tasks, you can use beam search
results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
:, :max_length
]
# Iterate over the results and get back the text
output_text = []
for res in results:
res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
output_text.append(res)
return output_text
# Let's check results on some validation samples
for batch in validation_dataset.take(1):
batch_images = batch["image"]
batch_labels = batch["label"]
preds = prediction_model.predict(batch_images)
pred_texts = decode_batch_predictions(preds)
orig_texts = []
for label in batch_labels:
label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
orig_texts.append(label)
_, ax = plt.subplots(4, 4, figsize=(15, 5))
for i in range(len(pred_texts)):
img = (batch_images[i, :, :, 0] * 255).numpy().astype(np.uint8)
img = img.T
title = f"Prediction: {pred_texts[i]}"
ax[i // 4, i % 4].imshow(img, cmap="gray")
ax[i // 4, i % 4].set_title(title)
ax[i // 4, i % 4].axis("off")
plt.show()
Model: "functional_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= image (InputLayer) [(None, 200, 50, 1)] 0 _________________________________________________________________ Conv1 (Conv2D) (None, 200, 50, 32) 320 _________________________________________________________________ pool1 (MaxPooling2D) (None, 100, 25, 32) 0 _________________________________________________________________ Conv2 (Conv2D) (None, 100, 25, 64) 18496 _________________________________________________________________ pool2 (MaxPooling2D) (None, 50, 12, 64) 0 _________________________________________________________________ reshape (Reshape) (None, 50, 768) 0 _________________________________________________________________ dense1 (Dense) (None, 50, 64) 49216 _________________________________________________________________ dropout (Dropout) (None, 50, 64) 0 _________________________________________________________________ bidirectional (Bidirectional (None, 50, 256) 197632 _________________________________________________________________ bidirectional_1 (Bidirection (None, 50, 128) 164352 _________________________________________________________________ dense2 (Dense) (None, 50, 20) 2580 ================================================================= Total params: 432,596 Trainable params: 432,596 Non-trainable params: 0 _________________________________________________________________
(訳注: 実験結果)
以上