I am extremely new to Tensorflow hence I won't be sure exactly what will you need to solve my issue. So do let me know if you need any additional information.
Basically I'm trying to run images through Sequential. Based on the tutorial on https://www.tensorflow.org/tutorials/images/classification, I am trying to plug and play onto my own dataset.
I'm currently stuck at the running my model using model.fit() where it gave me the following error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-90-85c03bda7f8f> in <module>
16
17 epochs=1
---> 18 history = model.fit(
19 train_data,
20 validation_data=test_data,
~/opt/anaconda3/lib/python3.8/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)
1132 _r=1):
1133 callbacks.on_train_batch_begin(step)
-> 1134 tmp_logs = self.train_function(iterator)
1135 if data_handler.should_sync:
1136 context.async_wait()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
816 tracing_count = self.experimental_get_tracing_count()
817 with trace.Trace(self._name) as tm:
--> 818 result = self._call(*args, **kwds)
819 compiler = "xla" if self._jit_compile else "nonXla"
820 new_tracing_count = self.experimental_get_tracing_count()
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
860 # This is the first call of __call__, so we have to initialize.
861 initializers = []
--> 862 self._initialize(args, kwds, add_initializers_to=initializers)
863 finally:
864 # At this point we know that the initialization is complete (or less
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
701 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
702 self._concrete_stateful_fn = (
--> 703 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
704 *args, **kwds))
705
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3018 args, kwargs = None, None
3019 with self._lock:
-> 3020 graph_function, _ = self._maybe_define_function(args, kwargs)
3021 return graph_function
3022
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3412
3413 self._function_cache.missed.add(call_context_key)
-> 3414 graph_function = self._create_graph_function(args, kwargs)
3415 self._function_cache.primary[cache_key] = graph_function
3416
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3247 arg_names = base_arg_names + missing_arg_names
3248 graph_function = ConcreteFunction(
-> 3249 func_graph_module.func_graph_from_py_func(
3250 self._name,
3251 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)
996 _, original_func = tf_decorator.unwrap(python_func)
997
--> 998 func_outputs = python_func(*func_args, **func_kwargs)
999
1000 # 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)
610 xla_context.Exit()
611 else:
--> 612 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
613 return out
614
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
983 except Exception as e: # pylint:disable=broad-except
984 if hasattr(e, "ag_error_metadata"):
--> 985 raise e.ag_error_metadata.to_exception(e)
986 else:
987 raise
ValueError: in user code:
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:839 train_function *
return step_function(self, iterator)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:829 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1262 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2734 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3423 _call_for_each_replica
return fn(*args, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:822 run_step **
outputs = model.train_step(data)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:788 train_step
y_pred = self(x, training=True)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1032 __call__
outputs = call_fn(inputs, *args, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:398 call
outputs = layer(inputs, **kwargs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1028 __call__
self._maybe_build(inputs)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:2722 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:188 build
input_channel = self._get_input_channel(input_shape)
/Users/mongchanghsi/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:367 _get_input_channel
raise ValueError('The channel dimension of the inputs '
ValueError: The channel dimension of the inputs should be defined. Found `None`.
Here is my code for the model:
model = Sequential([
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(4)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
epochs=10
history = model.fit(
train_data,
validation_data=test_data,
epochs=epochs
)
I understand that in the tutorial they used a inbuilt preprocessing function however I tried to build my own preprocessing function to facilitate my learning as well.
def preprocessing(image, target_size):
# Extracting labels
parts = tf.strings.split(image, os.sep)
label = parts[-2]
# Decoding image file
path = tf.io.read_file(image)
image = tf.image.decode_jpeg(path)
# Cropping
image = tf.image.crop_to_bounding_box(image, offset_height=25, offset_width=25, target_height=image_size, target_width=image_size)
# Normalizing
image = image / 255
return image, label
list_ds = tf.data.Dataset.list_files(DATA_DIR + '/*/*')
preprocess_function = partial(preprocessing, target_size=image_size)
processed_data = list_ds.map(preprocess_function)
train_data = processed_data.take(8000).batch(batch_size)
test_data = processed_data.skip(8000).batch(batch_size)
Other information that I can provide is that the images are of grey-scale hence 1 channel and I have normalized it /255 in my preprocessing function and the image_size is 300 and batch_size is 100.
