The problem being solved is a binary classification using CNN. Here I have images of different size. I am scaled down the images to 20% and normalizing them to the range (0,1) and obviously they still have different sizes. I am using x_train = tf.ragged.constant(normalized_train) to pass them into the CNN but I get an error as mentioned below -
ValueError: The channel dimension of the inputs should be defined. Found None.
Can anyone help?
Also, please suggest if I can use padding around the images to bring everything into same size without distorting the images.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Dropout, MaxPooling2D, Flatten, Dense
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=3, activation="relu"))
model.add(Conv2D(filters=64, kernel_size=3, activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.2))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
**X_train = tf.ragged.constant(normalized_train)** # allows to pass different size objects
y_train = tf.convert_to_tensor(y_train) # converting list type to tensor
**X_test = tf.ragged.constant(normalized_test)**
y_test = tf.convert_to_tensor(y_test) # converting list type to tensor
model.compile(loss="binary_crossentropy", metrics=\["accuracy"\], optimizer="adam")
model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs = 3)
Detailed error is:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-35-aa60c2e9ae0d> in <module>
1 model.compile(loss="binary_crossentropy", metrics=["accuracy"], optimizer="adam")
2 # model.fit(normalized_train, y_train, validation_data=(normalized_test,y_test), epochs = 3)
----> 3 model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs = 3)
/opt/anaconda3/lib/python3.7/site-packages/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)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
758 self._concrete_stateful_fn = (
759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 760 *args, **kwds))
761
762 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3306 arg_names=arg_names,
3307 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3308 capture_by_value=self._capture_by_value),
3309 self._function_attributes,
3310 function_spec=self.function_spec,
/opt/anaconda3/lib/python3.7/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, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py:787 train_step
y_pred = self(x, training=True)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/sequential.py:383 call
outputs = layer(inputs, **kwargs)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py:1030 __call__
self._maybe_build(inputs)
/opt/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py:2659 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
/opt/anaconda3/lib/python3.7/site-packages/keras/layers/convolutional.py:187 build
input_channel = self._get_input_channel(input_shape)
/opt/anaconda3/lib/python3.7/site-packages/keras/layers/convolutional.py:366 _get_input_channel
raise ValueError('The channel dimension of the inputs '
ValueError: The channel dimension of the inputs should be defined. Found `None`.