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 plt
from pathlib import Path
from collections import Counter
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
加载数据：验证码图像
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 % 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
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()
模型
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
__________________________________________________________________________________________________
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=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 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
_________________________________________________________________
