Python 3.x 什么是'；NotFoundError:没有算法工作'；？

前一段时间，我写了一个CNN代码来检测猫和狗。数据取自卡格尔，CNN代码成功运行。在此之前，为了加快学习速度，我安装了Nvidia Cuda。现在我从Kaggle（）找到了一个新任务。我又写了一次CNN代码。但我犯了一个从未见过的错误

代码：

输出：

C:\Users\Lenovo\anaconda3\python.exe "C:/Users/Lenovo/PycharmProjects/AI/1- Text.py"
2020-10-30 12:03:20.802426: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
Found 5216 images belonging to 2 classes.
Found 16 images belonging to 2 classes.
Found 624 images belonging to 2 classes.
data batch shape: (16, 1000, 1000, 3)
label batch shape: (16,)
2020-10-30 12:03:24.063892: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2020-10-30 12:03:25.009097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s
2020-10-30 12:03:25.009744: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-10-30 12:03:25.017137: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:25.022144: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-10-30 12:03:25.024962: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-10-30 12:03:25.030998: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-10-30 12:03:25.034229: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-10-30 12:03:25.052663: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:25.053092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-10-30 12:03:25.053825: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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.
2020-10-30 12:03:25.063634: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1dafce31430 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-30 12:03:25.064420: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-10-30 12:03:25.065036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s
2020-10-30 12:03:25.065861: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-10-30 12:03:25.066244: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:25.066580: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-10-30 12:03:25.066872: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-10-30 12:03:25.067182: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-10-30 12:03:25.067619: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-10-30 12:03:25.068034: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:25.068448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-10-30 12:03:25.734018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-30 12:03:25.734464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
2020-10-30 12:03:25.734664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
2020-10-30 12:03:25.735009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2984 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2020-10-30 12:03:25.738356: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1daa2f2ca40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-10-30 12:03:25.738781: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
WARNING:tensorflow:From C:/Users/Lenovo/PycharmProjects/AI/1- Text.py:66: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 997, 997, 32)      544       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 332, 332, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 329, 329, 64)      32832     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 109, 109, 64)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 106, 106, 64)      65600     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 32, 32, 128)       131200    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 10, 10, 128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 7, 7, 128)         262272    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 2, 2, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 256)               131328    
_________________________________________________________________
dense_1 (Dense)              (None, 256)               65792     
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 257       
=================================================================
Total params: 689,825
Trainable params: 689,825
Non-trainable params: 0
_________________________________________________________________
Epoch 1/30
2020-10-30 12:03:27.997615: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:28.951268: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:29.803616: W tensorflow/core/common_runtime/bfc_allocator.cc:246] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.91GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-10-30 12:03:29.804253: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once.
2020-10-30 12:03:29.805214: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at conv_ops.cc:1115 : Not found: No algorithm worked!
Traceback (most recent call last):
  File "C:/Users/Lenovo/PycharmProjects/AI/1- Text.py", line 66, in <module>
    history = model_.fit_generator(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1815, in fit_generator
    return self.fit(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit
    tmp_logs = train_function(iterator)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 840, in _call
    return self._stateless_fn(*args, **kwds)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1843, in _filtered_call
    return self._call_flat(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1923, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 545, in call
    outputs = execute.execute(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.NotFoundError:  No algorithm worked!
     [[node sequential/conv2d/Conv2D (defined at /Users/Lenovo/PycharmProjects/AI/1- Text.py:66) ]] [Op:__inference_train_function_1397]

Function call stack:
train_function

2020-10-30 12:03:29.893708: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated.
     [[{{node PyFunc}}]]

Process finished with exit code 1

C:\Users\Lenovo\anaconda3\python.exe“C:/Users/Lenovo/PycharmProjects/AI/1-Text.py”
2020-10-30 12:03:20.802426:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cudart64_101.dll
找到了5216张属于2类的图像。
发现了16幅属于2类的图像。
找到了624张属于2类的图片。
数据批形状：（16,1000,1000,3）
标签批次形状：（16，）
2020-10-30 12:03:24.063892:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库nvcuda.dll
2020-10-30 12:03:25.009097:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716]找到了具有以下属性的设备0：
pciBusID:0000:01:00.0名称：GeForce GTX 1050 Ti计算能力：6.1
核心时钟：1.62GHz核心计数：6个设备内存大小：4.00GiB设备内存带宽：104.43GiB/s
2020-10-30 12:03:25.009744:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cudart64_101.dll
2020-10-30 12:03:25.017137:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cublas64_10.dll
2020-10-30 12:03:25.022144:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cufft64_10.dll
2020-10-30 12:03:25.024962:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库库库和64_10.dll
2020-10-30 12:03:25.030998:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cusolver64_10.dll
2020-10-30 12:03:25.034229:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cusparse64_10.dll
2020-10-30 12:03:25.052663:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cudnn64_7.dll
2020-10-30 12:03:25.053092:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858]添加可见gpu设备：0
2020-10-30 12:03:25.053825:I tensorflow/core/platform/cpu_feature_guard.cc:142]此tensorflow二进制文件使用oneAPI深度神经网络库（oneDNN）进行优化，以便在性能关键型操作中使用以下cpu指令：AVX2
要在其他操作中启用它们，请使用适当的编译器标志重新生成TensorFlow。
2020-10-30 12:03: 25.063634：I TysFult/Cys/XLA/Service / Service。CC：168）XLA服务0x1DAFCE31430初始化为平台主机（这不能保证XLA将被使用）。设备：
2020-10-30 12:03:25.064420:I tensorflow/compiler/xla/service/service.cc:176]StreamExecutor设备（0）：主机，默认版本
2020-10-30 12:03:25.065036:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716]找到了具有以下属性的设备0：
pciBusID:0000:01:00.0名称：GeForce GTX 1050 Ti计算能力：6.1
核心时钟：1.62GHz核心计数：6个设备内存大小：4.00GiB设备内存带宽：104.43GiB/s
2020-10-30 12:03:25.065861:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cudart64_101.dll
2020-10-30 12:03:25.066244:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cublas64_10.dll
2020-10-30 12:03:25.066580:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cufft64_10.dll
2020-10-30 12:03:25.066872:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库库库和64_10.dll
2020-10-30 12:03:25.067182:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cusolver64_10.dll
2020-10-30 12:03:25.067619:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cusparse64_10.dll
2020-10-30 12:03:25.068034:I tensorflow/stream_executor/platform/default/dso_loader.cc:48]已成功打开动态库cudnn64_7.dll
2020-10-30 12:03:25.068448:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858]添加可见gpu设备：0
2020-10-30 12:03:25.734018:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257]设备互连拖缆执行器，带强度1边缘矩阵：
2020-10-30 12:03:25.734464:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]0
2020-10-30 12:03:25.734664:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276]0:N
2020-10-30 12:03:25.735009:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402]创建tensorflow设备（/job:localhost/replica:0/task:0/device:gpu:0，内存2984MB）>物理gpu（设备：0，名称：GeForce GTX 1050TI，pci总线id:0000:01:00.0，计算能力：6.1）
2020-10-30 12:03: 25.738356：I TysFult/Cys/XLA/Service /Service。CC：168）XLA服务0x1Daa2F2CA40初始化为平台CUDA（这不能保证XLA将被使用）。设备：
2020-10-30 12:03:25.738781:I tensorflow/compiler/xla/service/service.cc:176]流执行器设备（0）：GeForce GTX 1050 Ti，计算能力6.1
警告：tensorflow:From C:/Users/Lenovo/PycharmProjects/AI/1-Text.py:66:Model.fit_生成器（来自tensorflow.python.keras.engine.training）已被弃用，将在未来版本中删除。
更新说明：
请使用支持发电机的Model.fit。
模型：“顺序”
_________________________________________________________________
层（类型）输出形状参数
=================================================================
conv2d（conv2d）（无，997997，32）544
_________________________________________
C:\Users\Lenovo\anaconda3\python.exe "C:/Users/Lenovo/PycharmProjects/AI/1- Text.py"
2020-10-30 12:03:20.802426: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
Found 5216 images belonging to 2 classes.
Found 16 images belonging to 2 classes.
Found 624 images belonging to 2 classes.
data batch shape: (16, 1000, 1000, 3)
label batch shape: (16,)
2020-10-30 12:03:24.063892: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2020-10-30 12:03:25.009097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s
2020-10-30 12:03:25.009744: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-10-30 12:03:25.017137: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:25.022144: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-10-30 12:03:25.024962: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-10-30 12:03:25.030998: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-10-30 12:03:25.034229: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-10-30 12:03:25.052663: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:25.053092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-10-30 12:03:25.053825: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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.
2020-10-30 12:03:25.063634: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1dafce31430 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-30 12:03:25.064420: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-10-30 12:03:25.065036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s
2020-10-30 12:03:25.065861: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
2020-10-30 12:03:25.066244: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:25.066580: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll
2020-10-30 12:03:25.066872: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll
2020-10-30 12:03:25.067182: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll
2020-10-30 12:03:25.067619: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll
2020-10-30 12:03:25.068034: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:25.068448: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-10-30 12:03:25.734018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-10-30 12:03:25.734464: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
2020-10-30 12:03:25.734664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
2020-10-30 12:03:25.735009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2984 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2020-10-30 12:03:25.738356: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1daa2f2ca40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-10-30 12:03:25.738781: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
WARNING:tensorflow:From C:/Users/Lenovo/PycharmProjects/AI/1- Text.py:66: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 997, 997, 32)      544       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 332, 332, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 329, 329, 64)      32832     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 109, 109, 64)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 106, 106, 64)      65600     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 32, 32, 128)       131200    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 10, 10, 128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 7, 7, 128)         262272    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 2, 2, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 256)               131328    
_________________________________________________________________
dense_1 (Dense)              (None, 256)               65792     
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 257       
=================================================================
Total params: 689,825
Trainable params: 689,825
Non-trainable params: 0
_________________________________________________________________
Epoch 1/30
2020-10-30 12:03:27.997615: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll
2020-10-30 12:03:28.951268: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll
2020-10-30 12:03:29.803616: W tensorflow/core/common_runtime/bfc_allocator.cc:246] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.91GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-10-30 12:03:29.804253: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once.
2020-10-30 12:03:29.805214: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at conv_ops.cc:1115 : Not found: No algorithm worked!
Traceback (most recent call last):
  File "C:/Users/Lenovo/PycharmProjects/AI/1- Text.py", line 66, in <module>
    history = model_.fit_generator(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1815, in fit_generator
    return self.fit(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit
    tmp_logs = train_function(iterator)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 840, in _call
    return self._stateless_fn(*args, **kwds)
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1843, in _filtered_call
    return self._call_flat(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1923, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 545, in call
    outputs = execute.execute(
  File "C:\Users\Lenovo\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.NotFoundError:  No algorithm worked!
     [[node sequential/conv2d/Conv2D (defined at /Users/Lenovo/PycharmProjects/AI/1- Text.py:66) ]] [Op:__inference_train_function_1397]

Function call stack:
train_function

2020-10-30 12:03:29.893708: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated.
     [[{{node PyFunc}}]]

Process finished with exit code 1

sudo echo 'deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /' >> /etc/apt/sources.list.d/nvidia-machine-learning.list
sudo apt update

# dependency required for nvidia-cuda-toolkit
sudo apt install nvidia-compute-utils-455
sudo apt install nvidia-cuda-toolkit