I’m having trouble understanding how the happy model is supposed to be built.
the formula that we’re given to update very clearly shows that we should be adding the layers as individual members of the list array inside of the tf.keras.Sequential() function.
def happyModel():
input_shape = (1, 64,64,1)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
model = tf.keras.Sequential([
## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3
## Conv2D with 32 7x7 filters and stride of 1
## BatchNormalization for axis 3
## ReLU
## Max Pooling 2D with default parameters
## Flatten layer
## Dense layer with 1 unit for output & 'sigmoid' activation
# YOUR CODE STARTS HERE
# YOUR CODE ENDS HERE
])
but if I do that I get this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-70-f33284fd82fe> in <module>
1 happy_model = happyModel()
2 # Print a summary for each layer
----> 3 for layer in summary(happy_model):
4 print(layer)
5
~/work/release/W1A2/test_utils.py in summary(model)
30 result = []
31 for layer in model.layers:
---> 32 descriptors = [layer.__class__.__name__, layer.output_shape, layer.count_params()]
33 if (type(layer) == Conv2D):
34 descriptors.append(layer.padding)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in output_shape(self)
2177 """
2178 if not self._inbound_nodes:
-> 2179 raise AttributeError('The layer has never been called '
2180 'and thus has no defined output shape.')
2181 all_output_shapes = set(
AttributeError: The layer has never been called and thus has no defined output shape.
the dense line that I added was this: layers.Dense(1) with the activation as sigmoid.
Now if I change this code instead to this:
model = tf.keras.models.Sequential()
## Conv2D with 32 7x7 filters and stride of 1
function(),
## BatchNormalization for axis 3
function(),
## ReLU
function(),
## Max Pooling 2D with default parameters
function(),
## Flatten layer
function(),
## Dense layer with 1 unit for output & 'sigmoid' activation
layers.Dense(1)` with the activation as sigmoid
now this does work and I’m able to pass the first model. but then the problem is when trying to run this line
happy_model.fit(X_train, Y_train, epochs=10, batch_size=16)
I get this error:
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-75-a5c10c720821> in <module>
----> 1 happy_model.fit(X_train, Y_train, epochs=10, batch_size=16)
/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)
838 # Lifting succeeded, so variables are initialized and we can run the
839 # stateless function.
--> 840 return self._stateless_fn(*args, **kwds)
841 else:
842 canon_args, canon_kwds = \
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2827 with self._lock:
2828 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2830
2831 @property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs, cancellation_manager)
1846 resource_variable_ops.BaseResourceVariable))],
1847 captured_inputs=self.captured_inputs,
-> 1848 cancellation_manager=cancellation_manager)
1849
1850 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1922 # No tape is watching; skip to running the function.
1923 return self._build_call_outputs(self._inference_function.call(
-> 1924 ctx, args, cancellation_manager=cancellation_manager))
1925 forward_backward = self._select_forward_and_backward_functions(
1926 args,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
548 inputs=args,
549 attrs=attrs,
--> 550 ctx=ctx)
551 else:
552 outputs = execute.execute_with_cancellation(
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
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:
62 if name is not None:
InvalidArgumentError: Incompatible shapes: [16,64,64] vs. [16,1]
[[node Equal (defined at <ipython-input-75-a5c10c720821>:1) ]] [Op:__inference_train_function_2028]
Function call stack:
train_function
and I have no idea how to fix this.