michalmarsalek / pecaptcha Goto Github PK

WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'tuple'> input: (<tf.Tensor 'IteratorGetNext:0' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 24, 1) dtype=uint8>)
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Model was constructed with shape (None, 24, 24, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 24, 24, 1), dtype=tf.float32, name='rescaling_input'), name='rescaling_input', description="created by layer 'rescaling_input'"), but it was called on an input with incompatible shape (None, 24, 1, 1).
Traceback (most recent call last):
  File "test_from_disk.py", line 3, in <module>
    print("Success rate", pe_captcha.test_from_disk())
  File "/home/boxxo/Desktop/environments/PEcaptcha/pe_captcha/__init__.py", line 216, in test_from_disk
    return correct/total
ZeroDivisionError: division by zero

Traceback (most recent call last):
  File "test.py", line 13, in <module>
    pred, _, _ = pe_captcha.solve(model, im)
  File "/home/boxxo/Desktop/environments/PEcaptcha/pe_captcha/__init__.py", line 179, in solve
    prediction_vectors = model.predict(chars)
  File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/engine/data_adapter.py", line 245, in __init__
    num_samples = set(int(i.shape[0]) for i in tf.nest.flatten(inputs)).pop()
KeyError: 'pop from an empty set'

WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory.
2021-11-30 16:30:10.374403: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/cv2/../../lib64:
2021-11-30 16:30:10.374475: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2021-11-30 16:30:10.374512: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (boxxo-vb): /proc/driver/nvidia/version does not exist
2021-11-30 16:30:10.374909: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'tuple'> input: (<tf.Tensor 'IteratorGetNext:0' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 24, 1) dtype=uint8>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 24, 1) dtype=uint8>)
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Model was constructed with shape (None, 24, 24, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 24, 24, 1), dtype=tf.float32, name='rescaling_input'), name='rescaling_input', description="created by layer 'rescaling_input'"), but it was called on an input with incompatible shape (None, 24, 1, 1).
Traceback (most recent call last):
  File "collect_training.py", line 3, in <module>
    pe_captcha.collect_training("model_t", "model0")
  File "/home/boxxo/Desktop/environments/PEcaptcha/pe_captcha/__init__.py", line 64, in collect_training
    pred, _, _ = solve(old_model, img) if old_model else None
  File "/home/boxxo/Desktop/environments/PEcaptcha/pe_captcha/__init__.py", line 179, in solve
    prediction_vectors = model.predict(chars)
  File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 1129, in autograph_handler
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/engine/training.py", line 1621, in predict_function  *
        return step_function(self, iterator)
    File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/engine/training.py", line 1611, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/engine/training.py", line 1604, in run_step  **
        outputs = model.predict_step(data)
    File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/engine/training.py", line 1572, in predict_step
        return self(x, training=False)
    File "/home/boxxo/Desktop/environments/PEcaptcha/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None

    ValueError: Exception encountered when calling layer "max_pooling2d" (type MaxPooling2D).
    
    Negative dimension size caused by subtracting 2 from 1 for '{{node sequential/max_pooling2d/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", explicit_paddings=[], ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1]](sequential/conv2d/Relu)' with input shapes: [?,24,1,32].
    
    Call arguments received:
      • inputs=tf.Tensor(shape=(None, 24, 1, 32), dtype=float32)