ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (2240, 70, 3)

Question

I am working on CNN-LSTM model for training and my dataset contains 25760 images with dimension (70,70,3). During training I came across this error: "ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (2240, 70, 3)".

Can anyone tell me what does it mean and how to solve it?

Shape of train data:(25760,70,70,3)

Code:

import tensorflow as tf
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, LSTM, TimeDistributed

print(np.shape(X))

u=np.array(X)
v=np.array(y)

           
model=Sequential()
            
model.add(TimeDistributed(Conv2D(32,(5,5),padding='same',input_shape=(70,70,3))))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))
            
model.add(TimeDistributed(Conv2D(32,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))

model.add(TimeDistributed(Conv2D(64,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))

model.add(TimeDistributed(Conv2D(64,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))

model.add(TimeDistributed(Conv2D(128,(5,5),padding='same')))
model.add(TimeDistributed(Activation('relu')))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2))))

model.add(Flatten())

model.add(LSTM(100))

model.add(Dense(64))
model.add(Activation('relu'))

model.add(Dense(8))
model.add(Activation('softmax'))

model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=['accuracy'])

model.fit(u,v,batch_size=32,epochs=20,validation_split=0.2)
          
model.save('cnn_lstm_1.h5')

Traceback:

ValueError                                Traceback (most recent call last)
<ipython-input-5-c86147b70dfb> in <module>
     46 model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=['accuracy'])
     47 
---> 48 model.fit(u,v,batch_size=32,epochs=20,validation_split=0.2)
     49 
     50 model.save('cnn_lstm_1.h5')

/opt/conda/lib/python3.7/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()

/opt/conda/lib/python3.7/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()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    869       # This is the first call of __call__, so we have to initialize.
    870       initializers = []
--> 871       self._initialize(args, kwds, add_initializers_to=initializers)
    872     finally:
    873       # At this point we know that the initialization is complete (or less

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    724     self._concrete_stateful_fn = (
    725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 726             *args, **kwds))
    727 
    728     def invalid_creator_scope(*unused_args, **unused_kwds):

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2967       args, kwargs = None, None
   2968     with self._lock:
-> 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
   2970     return graph_function
   2971 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3359 
   3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
   3362           self._function_cache.primary[cache_key] = graph_function
   3363 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3204             arg_names=arg_names,
   3205             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206             capture_by_value=self._capture_by_value),
   3207         self._function_attributes,
   3208         function_spec=self.function_spec,

/opt/conda/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)
    988         _, original_func = tf_decorator.unwrap(python_func)
    989 
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
    991 
    992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    632             xla_context.Exit()
    633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    635         return out
    636 

/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

ValueError: in user code:

    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
        outputs = model.train_step(data)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:754 train_step
        y_pred = self(x, training=True)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py:389 call
        outputs = layer(inputs, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/wrappers.py:241 call
        y = self.layer(inputs, **kwargs)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py:239 assert_input_compatibility
        str(tuple(shape)))

    ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (2240, 70, 3)

Answer 1

You are feeding sequence of images right?

If so, then your dataset should contain following dimensions batch_size x sequence_length x img_width x img_height x channels

for example a typical batch of size 32 and sequence length of 5 would be would be 32x5x70x70x3

You can convert your dataset using pad_sequences() function to make your dataset shape to be training batch shape. Refer keras pad_sequences