#機器視覺
import tensorflow as tf
from tensorflow import keras
fashion_mnist=keras.datasets.fashion_mnist
(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()
#資料下載
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 15us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [==============================] - 1319s 50us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
8192/5148 [===============================================] - 0s 10us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4423680/4422102 [==============================] - 191s 43us/step
#建模
model=keras.Sequential()
model.add(keras.layers.Flatten(input_shape=(28,28)))
model.add(keras.layers.Dense(128,activation=tf.nn.relu))
model.add(keras.layers.Dense(10,activation=tf.nn.softmax))
#訓練
train_images=train_images/255
model.compile(optimizer=tf.optimizers.Adam(),loss=tf.losses.sparse_categorical_crossentropy,metrics=['accuracy'])
model.fit(train_images,train_labels,epochs=10)
Epoch 1/10
1875/1875 [==============================] - 1s 675us/step - loss: 2.3029 - accuracy: 0.1014
Epoch 2/10
1875/1875 [==============================] - 1s 647us/step - loss: 2.3028 - accuracy: 0.0994
Epoch 3/10
1875/1875 [==============================] - 1s 641us/step - loss: 2.3028 - accuracy: 0.1011
Epoch 4/10
1875/1875 [==============================] - 1s 644us/step - loss: 2.3028 - accuracy: 0.0996
Epoch 5/10
1875/1875 [==============================] - 1s 637us/step - loss: 2.3027 - accuracy: 0.1017
Epoch 6/10
1875/1875 [==============================] - 1s 642us/step - loss: 2.3029 - accuracy: 0.0998
Epoch 7/10
1875/1875 [==============================] - 1s 644us/step - loss: 2.3028 - accuracy: 0.1017
Epoch 8/10
1875/1875 [==============================] - 1s 652us/step - loss: 2.3027 - accuracy: 0.1010
Epoch 9/10
1875/1875 [==============================] - 1s 645us/step - loss: 2.3027 - accuracy: 0.1014
Epoch 10/10
1875/1875 [==============================] - 1s 644us/step - loss: 2.3026 - accuracy: 0.1010
<tensorflow.python.keras.callbacks.History at 0x160584760>
#訓練測驗
test_images_scaled=test_images/255
model.evaluate(test_images_scaled,test_labels)
313/313 [==============================] - 0s 535us/step - loss: 589.2147 - accuracy: 0.2129
[589.2147216796875, 0.21289999783039093]
#模型測驗
import numpy as np
np.argmax(model.predict(train_images[8]))
#這為毛就報錯了呢？想了老半天
ValueError Traceback (most recent call last)
<ipython-input-24-4b3b43af4905> in <module>
----> 1 model.predict(test_images[8])
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
128 raise ValueError('{} is not supported in multi-worker mode.'.format(
129 method.__name__))
--> 130 return method(self, *args, **kwargs)
131
132 return tf_decorator.make_decorator(
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1597 for step in data_handler.steps():
1598 callbacks.on_predict_batch_begin(step)
-> 1599 tmp_batch_outputs = predict_function(iterator)
1600 if data_handler.should_sync:
1601 context.async_wait()
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
812 # In this case we have not created variables on the first call. So we can
813 # run the first trace but we should fail if variables are created.
--> 814 results = self._stateful_fn(*args, **kwds)
815 if self._created_variables:
816 raise ValueError("Creating variables on a non-first call to a function"
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2826 """Calls a graph function specialized to the inputs."""
2827 with self._lock:
-> 2828 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2829 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2830
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3208 and self.input_signature is None
3209 and call_context_key in self._function_cache.missed):
-> 3210 return self._define_function_with_shape_relaxation(args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _define_function_with_shape_relaxation(self, args, kwargs)
3139 expand_composites=True)
3140
-> 3141 graph_function = self._create_graph_function(
3142 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3143 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,
3067 self._python_function,
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1462 predict_function *
return step_function(self, iterator)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1452 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1445 run_step **
outputs = model.predict_step(data)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1418 predict_step
return self(x, training=False)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:372 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py:385 call
return self._run_internal_graph(
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:975 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs,
/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:212 assert_input_compatibility
raise ValueError(
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None, 28]
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標籤：機器視覺