results.utf8.md

其中第一個模型 model 1 是 keras 的範例，model 2 至 model 5 是自行設定的第二個到第五個模型。第二和第三個模型的準確度比第一個模型更高。第四個模型略低於第一個模型，第五個模型的準確度很低，是劣質的模型，這也告訴我們並不是隨意設定神經網路就能訓練出表現優異的模型。
Models	model 1	model 2	model 3	model 4	model 5
Loss	0.0264	0.0269	0.0237	0.0521	1.6944
Accuracy	0.9913	0.9923	0.9934	0.9901	0.2622
model = load_model('cnn_model_example.h5')
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
#> Test loss: 0.02636140756745326
print('Test accuracy:', score[1])
#> Test accuracy: 0.9912999868392944
model_2 = load_model('cnn_model_2.h5')
score = model_2.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
#> Test loss: 0.02687886219765009
print('Test accuracy:', score[1])
#> Test accuracy: 0.9927999973297119
model_3 = load_model('cnn_model_3.h5')
score = model_3.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
#> Test loss: 0.023742247533547946
print('Test accuracy:', score[1])
#> Test accuracy: 0.993399977684021
model_4 = load_model('cnn_model_4.h5')
#> --- Logging error ---
#> Traceback (most recent call last):
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\logging\__init__.py", line 996, in emit
#>     stream.write(msg)
#> ValueError: I/O operation on closed file
#> Call stack:
#>   File "<string>", line 1, in <module>
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
#>     return load_function(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 584, in load_model
#>     model = _deserialize_model(h5dict, custom_objects, compile)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 274, in _deserialize_model
#>     model = model_from_config(model_config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 627, in model_from_config
#>     return deserialize(config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\__init__.py", line 168, in deserialize
#>     printable_module_name='layer')
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\utils\generic_utils.py", line 147, in deserialize_keras_object
#>     list(custom_objects.items())))
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 302, in from_config
#>     model.add(layer)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 182, in add
#>     output_tensor = layer(self.outputs[0])
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
#>     output = self.call(inputs, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 127, in call
#>     training=training)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3214, in in_train_phase
#>     x = switch(training, x, alt)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3147, in switch
#>     else_expression_fn)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1389, in cond_for_tf_v2
#>     return cond(pred, true_fn=true_fn, false_fn=false_fn, strict=True, name=name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1174, in cond
#>     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 84, in cond_v2
#>     op_return_value=pred)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
#>     func_outputs = python_func(*func_args, **func_kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 125, in dropped_inputs
#>     seed=self.seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3492, in dropout
#>     return tf.nn.dropout(x, rate=level, noise_shape=noise_shape, seed=seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 4283, in dropout_v2
#>     "Please ensure that this is intended.", 5, rate)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 248, in log_first_n
#>     log_if(level, msg, count < n, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 255, in log_if
#>     vlog(level, msg, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 200, in vlog
#>     get_logger().log(level, msg, *args, **kwargs)
#> Message: 'Large dropout rate: %g (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.'
#> Arguments: (0.75,)
score = model_4.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
#> Test loss: 0.05209506639393801
print('Test accuracy:', score[1])
#> Test accuracy: 0.9901000261306763
model_5 = load_model('cnn_model_5.h5')
#> --- Logging error ---
#> Traceback (most recent call last):
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\logging\__init__.py", line 996, in emit
#>     stream.write(msg)
#> ValueError: I/O operation on closed file
#> Call stack:
#>   File "<string>", line 1, in <module>
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
#>     return load_function(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 584, in load_model
#>     model = _deserialize_model(h5dict, custom_objects, compile)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 274, in _deserialize_model
#>     model = model_from_config(model_config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 627, in model_from_config
#>     return deserialize(config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\__init__.py", line 168, in deserialize
#>     printable_module_name='layer')
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\utils\generic_utils.py", line 147, in deserialize_keras_object
#>     list(custom_objects.items())))
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 302, in from_config
#>     model.add(layer)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 182, in add
#>     output_tensor = layer(self.outputs[0])
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
#>     output = self.call(inputs, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 127, in call
#>     training=training)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3214, in in_train_phase
#>     x = switch(training, x, alt)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3147, in switch
#>     else_expression_fn)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1389, in cond_for_tf_v2
#>     return cond(pred, true_fn=true_fn, false_fn=false_fn, strict=True, name=name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1174, in cond
#>     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 84, in cond_v2
#>     op_return_value=pred)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
#>     func_outputs = python_func(*func_args, **func_kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 125, in dropped_inputs
#>     seed=self.seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3492, in dropout
#>     return tf.nn.dropout(x, rate=level, noise_shape=noise_shape, seed=seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 4283, in dropout_v2
#>     "Please ensure that this is intended.", 5, rate)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 248, in log_first_n
#>     log_if(level, msg, count < n, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 255, in log_if
#>     vlog(level, msg, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 200, in vlog
#>     get_logger().log(level, msg, *args, **kwargs)
#> Message: 'Large dropout rate: %g (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.'
#> Arguments: (0.8,)
#> --- Logging error ---
#> Traceback (most recent call last):
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\logging\__init__.py", line 996, in emit
#>     stream.write(msg)
#> ValueError: I/O operation on closed file
#> Call stack:
#>   File "<string>", line 1, in <module>
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
#>     return load_function(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 584, in load_model
#>     model = _deserialize_model(h5dict, custom_objects, compile)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 274, in _deserialize_model
#>     model = model_from_config(model_config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 627, in model_from_config
#>     return deserialize(config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\__init__.py", line 168, in deserialize
#>     printable_module_name='layer')
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\utils\generic_utils.py", line 147, in deserialize_keras_object
#>     list(custom_objects.items())))
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 302, in from_config
#>     model.add(layer)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 182, in add
#>     output_tensor = layer(self.outputs[0])
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
#>     output = self.call(inputs, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 127, in call
#>     training=training)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3214, in in_train_phase
#>     x = switch(training, x, alt)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3147, in switch
#>     else_expression_fn)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1389, in cond_for_tf_v2
#>     return cond(pred, true_fn=true_fn, false_fn=false_fn, strict=True, name=name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1174, in cond
#>     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 84, in cond_v2
#>     op_return_value=pred)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
#>     func_outputs = python_func(*func_args, **func_kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 125, in dropped_inputs
#>     seed=self.seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3492, in dropout
#>     return tf.nn.dropout(x, rate=level, noise_shape=noise_shape, seed=seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 4283, in dropout_v2
#>     "Please ensure that this is intended.", 5, rate)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 248, in log_first_n
#>     log_if(level, msg, count < n, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 255, in log_if
#>     vlog(level, msg, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 200, in vlog
#>     get_logger().log(level, msg, *args, **kwargs)
#> Message: 'Large dropout rate: %g (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.'
#> Arguments: (0.7,)
#> --- Logging error ---
#> Traceback (most recent call last):
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\logging\__init__.py", line 996, in emit
#>     stream.write(msg)
#> ValueError: I/O operation on closed file
#> Call stack:
#>   File "<string>", line 1, in <module>
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
#>     return load_function(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 584, in load_model
#>     model = _deserialize_model(h5dict, custom_objects, compile)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 274, in _deserialize_model
#>     model = model_from_config(model_config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\saving.py", line 627, in model_from_config
#>     return deserialize(config, custom_objects=custom_objects)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\__init__.py", line 168, in deserialize
#>     printable_module_name='layer')
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\utils\generic_utils.py", line 147, in deserialize_keras_object
#>     list(custom_objects.items())))
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 302, in from_config
#>     model.add(layer)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\sequential.py", line 182, in add
#>     output_tensor = layer(self.outputs[0])
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\engine\base_layer.py", line 489, in __call__
#>     output = self.call(inputs, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 127, in call
#>     training=training)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3214, in in_train_phase
#>     x = switch(training, x, alt)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3147, in switch
#>     else_expression_fn)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1389, in cond_for_tf_v2
#>     return cond(pred, true_fn=true_fn, false_fn=false_fn, strict=True, name=name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 507, in new_func
#>     return func(*args, **kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\control_flow_ops.py", line 1174, in cond
#>     return cond_v2.cond_v2(pred, true_fn, false_fn, name)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 84, in cond_v2
#>     op_return_value=pred)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
#>     func_outputs = python_func(*func_args, **func_kwargs)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\layers\core.py", line 125, in dropped_inputs
#>     seed=self.seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3492, in dropout
#>     return tf.nn.dropout(x, rate=level, noise_shape=noise_shape, seed=seed)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 4283, in dropout_v2
#>     "Please ensure that this is intended.", 5, rate)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 248, in log_first_n
#>     log_if(level, msg, count < n, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 255, in log_if
#>     vlog(level, msg, *args)
#>   File "C:\Users\USER\ANACON~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\platform\tf_logging.py", line 200, in vlog
#>     get_logger().log(level, msg, *args, **kwargs)
#> Message: 'Large dropout rate: %g (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.'
#> Arguments: (0.6,)
score = model_5.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
#> Test loss: 2.3026296684265137
print('Test accuracy:', score[1])
#> Test accuracy: 0.10090000182390213