Hi there, I am working on the 1st week’s 2nd assignment since 2 days. But I ended up with an error that i am unable to solve.
Can anyone help me solve this…
Can anyone help me solve this…
Hi SriSurya,
When you use the functional API, you are not creating a list of layers (as in the sequential API). Instead you are assigning a layer, or an application of a layer to a tensor, to a variable. And this a number of times so as to create your functional model. Think about what this means for the details of your code.
2 Likes
Thank you reinoudbosch, figured it out
Epoch 1/100
ValueError Traceback (most recent call last)
in
1 train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)).batch(64)
2 test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)).batch(64)
----> 3 history = conv_model.fit(train_dataset, epochs=100, validation_data=test_dataset)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
→ 108 return method(self, *args, **kwargs)
109
110 # Running inside run_distribute_coordinator already.
/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)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
→ 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
/opt/conda/lib/python3.7/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/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821 # This is the first call of call, so we have to initialize.
822 initializers =
→ 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # 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)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
→ 697 *args, **kwds))
698
699 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)
2853 args, kwargs = None, None
2854 with self._lock:
→ 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
→ 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
→ 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 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)
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/conda/lib/python3.7/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/conda/lib/python3.7/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/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 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:2585 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:2945 _call_for_each_replica
return fn(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:749 train_step
y, y_pred, sample_weight, regularization_losses=self.losses)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:149 __call__
losses = ag_call(y_true, y_pred)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:253 call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:1535 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
return target(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/backend.py:4687 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 6) are incompatible
I am getting this error. How I can solve it?
Problem Solved. Just had to refresh my kernel.
1 Like
Can you please let me know how you solved ?i am having same issues
Try rerunning the entire notebook. I had the same issue and rerunning resolve the issue for me.
1 Like
YOUR CODE STARTS HERE
Z1 = tfl.Conv2D(8,(4,4), strides =(1,1), padding = 'same')(input_img),
A1 = tfl.ReLU()(Z1),
P1 = tfl.MaxPooling2D(pool_size=(8,8), strides = (8,8), padding = 'same')(A1),
Z2 = tfl.Conv2D(16,(2,2), strides =(1,1), padding = 'same')(P1),
A2 = tfl.ReLU()(Z2),
P2 = tfl.MaxPool2D(pool_size=(4,4),strides =(4,4), padding = 'same')(A2),
F = tfl.Flatten()(P2),
outputs = tf.keras.layers.Dense(units = 6,activation = 'softmax')(F),
# YOUR CODE ENDS HERE
here is the output error:
Input 0 of layer max_pooling2d_2 is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: [1, None, 64, 64, 8].
Error on line :
38 Z1 = tfl.Conv2D(8,(4,4), strides =(1,1), padding = ‘same’)(input_img),
39 A1 = tfl.ReLU()(Z1),
—> 40 P1 = tfl.MaxPooling2D(pool_size=(8,8), strides = (8,8), padding = ‘same’)(A1),
41 Z2 = tfl.Conv2D(16,(2,2), strides =(1,1), padding = ‘same’)(P1),
42 A2 = tfl.ReLU()(Z2),