I am trying to make an image classifier CNN using TensorFlow. I am trying to load the dataset using a ImageDataGenerator. Like this:
from tensorflow.keras.preprocessing.image import ImageDataGenerator
image_datagen = ImageDataGenerator(rescale=1/255)
IMAGE_DIMS=(200,200)
train_generator = image_datagen.flow_from_directory(
TRAIN_DIR,
target_size=IMAGE_DIMS,
batch_size=80,
class_mode="categorical",
color_mode="grayscale",
shuffle=True
)
model architecture:
model = keras.models.Sequential([
keras.layers.Conv2D(16,(3,3), input_shape=(200,200,1), activation='relu'),
keras.layers.MaxPooling2D(2, 2),
keras.layers.Conv2D(32,(3,3), activation='relu'),
keras.layers.MaxPooling2D(2, 2),
keras.layers.Conv2D(64,(3,3), activation='relu'),
keras.layers.MaxPooling2D(2, 2),
keras.layers.Flatten(),
keras.layers.Dense(units=512, activation="relu"),
keras.layers.Dense(units=16, activation="softmax")
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
I am loading an image dataset that is 200x200 pixels and greyscaled. There are 16 labels for the dataset(or the images are contained in 16 different folders). Loading the dataset works properly:
print(train_generator.labels)
print(train_generator.image_shape)
[ 0 0 0 ... 15 15 15]
(200, 200, 1)
After running:
model.fit(
train_generator,
steps_per_epoch=4,
epochs=2
)
I am getting this error:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-124-95f52517d8f4> in <module>
----> 1 model.fit(
2 train_generator,
3 steps_per_epoch=4,
4 epochs=2
5 )
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
2940 (graph_function,
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
-> 2942 return graph_function._call_flat(
2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
2944
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1916 and executing_eagerly):
1917 # No tape is watching; skip to running the function.
-> 1918 return self._build_call_outputs(self._inference_function.call(
1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
553 with _InterpolateFunctionError(self):
554 if cancellation_manager is None:
--> 555 outputs = execute.execute(
556 str(self.signature.name),
557 num_outputs=self._num_outputs,
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [80,16] and labels shape [1280]
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-124-95f52517d8f4>:1) ]] [Op:__inference_train_function_19777]
Function call stack:
train_function
I am using jupyter Ipython notebook.
I am relatively new to TensorFlow.
What is the error about? How do I fix this issue?
