Use Yukariin's better model

model_gen/captcha_75_25.h5

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-size 47170376
+oid sha256:61e6c4d5689ecaf2056fa81f7e02163371dbdace2dba0527069fe21cb5bf98c5
+size 791405

model_gen/penis.py

@@ -0,0 +1,47 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+from pathlib import Path
+from collections import Counter
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+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
+
+
+print("loading");
+model = tf.keras.saving.load_model("captcha_75_25.tf", custom_objects={'CTCLayer': CTCLayer});
+
+print("extracting");
+pmodel = keras.models.Model(
+    model.get_layer(name="image").input, model.get_layer(name="dense2").output
+)
+print("saving");
+
+tf.keras.saving.save_model(pmodel, "pcaptcha_75_25.keras")
+tf.keras.saving.save_model(pmodel, "pcaptcha_75_25.h5")
+
+print("done");

src/group1-shard1of1.bin

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