python识别图片验证码（用于读取验证码的OCR模型）

介绍python如何识别图片验证码？此python识别图片验证码示例演示了一个使用 Functional API 构建的简单 OCR 模型。除了结合 CNN 和 RNN 之外，它还说明了如何实例化一个新层并将其用作“端点层”来实现 CTC 损失。有关层子类化的详细指南，请查看开发人员指南中的此页面。
python读取验证码OCR模型：设置和安装

import os import numpy as np import matplotlib.pyplot as pltfrom pathlib import Path from collections import Counterimport tensorflow as tf from tensorflow import keras from tensorflow.keras import layers

加载数据：验证码图像【python识别图片验证码（用于读取验证码的OCR模型）】让我们下载数据。

!curl -LO https://github.com/AakashKumarNain/CaptchaCracker/raw/master/captcha_images_v2.zip !unzip -qq captcha_images_v2.zip

% Total% Received % XferdAverage SpeedTimeTimeTimeCurrent DloadUploadTotalSpentLeftSpeed 100159100159001640 --:--:-- --:--:-- --:--:--164 100 8863k100 8863k004882k00:00:010:00:01 --:--:-- 33.0M

python识别图片验证码示例：该数据集包含 1040 个验证码文件作为png图像。每个样本的标签是一个字符串，即文件名（减去文件扩展名）。我们将字符串中的每个字符映射到一个整数来训练模型。类似地，我们需要将模型的预测映射回字符串。为此，我们将维护两个字典，分别将字符映射到整数，以及将整数映射到字符。

# 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'}

python读取验证码OCR模型：预处理

# 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}

创建Dataset对象

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) )

可视化数据

_, 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()

文章图片
模型

class CTCLayer(layers.Layer): def __init__(self, name=None): super().__init__(name=name) self.loss_fn = keras.backend.ctc_batch_costdef 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_preddef 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 ShapeParam #Connected to ================================================================================================== image (InputLayer)[ (None, 200, 50, 1)] 0 __________________________________________________________________________________________________ Conv1 (Conv2D)(None, 200, 50, 32)320image[ 0][ 0] __________________________________________________________________________________________________ pool1 (MaxPooling2D)(None, 100, 25, 32)0Conv1[ 0][ 0] __________________________________________________________________________________________________ Conv2 (Conv2D)(None, 100, 25, 64)18496pool1[ 0][ 0] __________________________________________________________________________________________________ pool2 (MaxPooling2D)(None, 50, 12, 64)0Conv2[ 0][ 0] __________________________________________________________________________________________________ reshape (Reshape)(None, 50, 768)0pool2[ 0][ 0] __________________________________________________________________________________________________ dense1 (Dense)(None, 50, 64)49216reshape[ 0][ 0] __________________________________________________________________________________________________ dropout (Dropout)(None, 50, 64)0dense1[ 0][ 0] __________________________________________________________________________________________________ bidirectional (Bidirectional)(None, 50, 256)197632dropout[ 0][ 0] __________________________________________________________________________________________________ bidirectional_1 (Bidirectional) (None, 50, 128)164352bidirectional[ 0][ 0] __________________________________________________________________________________________________ label (InputLayer)[ (None, None)]0 __________________________________________________________________________________________________ dense2 (Dense)(None, 50, 20)2580bidirectional_1[ 0][ 0] __________________________________________________________________________________________________ ctc_loss (CTCLayer)(None, 50, 20)0label[ 0][ 0] dense2[ 0][ 0] ================================================================================================== Total params: 432,596 Trainable params: 432,596 Non-trainable params: 0 __________________________________________________________________________________________________

python如何识别图片验证码？训练

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=https://www.lsbin.com/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

python识别图片验证码示例 - 推理

# 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 ShapeParam # ================================================================= 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 _________________________________________________________________