help me in rectifying the error
GRADED FUNCTION: convolutional_model
def convolutional_model(input_shape):
“”"
Implements the forward propagation for the model:
CONV2D → RELU → MAXPOOL → CONV2D → RELU → MAXPOOL → FLATTEN → DENSE
Note that for simplicity and grading purposes, you'll hard-code some values
such as the stride and kernel (filter) sizes.
Normally, functions should take these values as function parameters.
Arguments:
input_img -- input dataset, of shape (input_shape)
Returns:
model -- TF Keras model (object containing the information for the entire training process)
"""
input_img = tf.keras.Input(shape=input_shape)
## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME'
# Z1 = None
## RELU
# A1 = None
## MAXPOOL: window 8x8, stride 8, padding 'SAME'
# P1 = None
## CONV2D: 16 filters 2x2, stride 1, padding 'SAME'
# Z2 = None
## RELU
# A2 = None
## MAXPOOL: window 4x4, stride 4, padding 'SAME'
# P2 = None
## FLATTEN
# F = None
## Dense layer
## 6 neurons in output layer. Hint: one of the arguments should be "activation='softmax'"
# outputs = None
# YOUR CODE STARTS HERE
ReLU=tf.keras.layers.ReLU()
Z1 =tf.keras.layers.Conv2D(8,4,strides=1,padding="SAME")(input_img)
A1=ReLU(Z1)
MP1=tf.keras.layers.MaxPool2D(pool_size=(8, 8), strides=8, padding="SAME")
P1=MP1(Z1)
Z2 =tf.keras.layers.Conv2D(16,2,strides=1,padding="SAME")(P1),
A2=ReLU(Z2)
MP2=tf.keras.layers.MaxPool2D(pool_size=(4, 4), strides=4, padding="SAME")
P2=MP2(Z2)
P2=tf.keras.layers.Flatten()(P2)
outputs = tfl.Dense (units= 6,activation="softmax")(P2)
# YOUR CODE ENDS HERE
model = tf.keras.Model(inputs=input_img, outputs=outputs)
return model
error:
InvalidArgumentError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1811 try:
→ 1812 c_op = pywrap_tf_session.TF_FinishOperation(op_desc)
1813 except errors.InvalidArgumentError as e:
InvalidArgumentError: Shape must be rank 4 but is rank 5 for ‘{{node max_pooling2d_8/MaxPool}} = MaxPoolT=DT_FLOAT, data_format=“NHWC”, ksize=[1, 4, 4, 1], padding=“SAME”, strides=[1, 4, 4, 1]’ with input shapes: [1,?,8,8,16].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
in
----> 1 conv_model = convolutional_model((64, 64, 3))
2 conv_model.compile(optimizer=‘adam’,
3 loss=‘categorical_crossentropy’,
4 metrics=[‘accuracy’])
5 conv_model.summary()
in convolutional_model(input_shape)
44 A2=ReLU(Z2)
45 MP2=tf.keras.layers.MaxPool2D(pool_size=(4, 4), strides=4, padding=“SAME”)
—> 46 P2=MP2(Z2)
47 P2=tf.keras.layers.Flatten()(P2)
48 outputs = tfl.Dense (units= 6,activation=“softmax”)(P2)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in call(self, *args, **kwargs)
924 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
925 return self._functional_construction_call(inputs, args, kwargs,
→ 926 input_list)
927
928 # Maintains info about the Layer.call stack.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1115 try:
1116 with ops.enable_auto_cast_variables(self._compute_dtype_object):
→ 1117 outputs = call_fn(cast_inputs, *args, **kwargs)
1118
1119 except errors.OperatorNotAllowedInGraphError as e:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/pooling.py in call(self, inputs)
294 strides=strides,
295 padding=self.padding.upper(),
→ 296 data_format=conv_utils.convert_data_format(self.data_format, 4))
297 return outputs
298
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
199 “”“Call target, and fall back on dispatchers if there is a TypeError.”""
200 try:
→ 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py in max_pool(value, ksize, strides, padding, data_format, name, input)
4519 padding=padding,
4520 data_format=data_format,
→ 4521 name=name)
4522
4523
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_nn_ops.py in max_pool(input, ksize, strides, padding, data_format, name)
5267 _, _, _op, _outputs = _op_def_library._apply_op_helper(
5268 “MaxPool”, input=input, ksize=ksize, strides=strides, padding=padding,
→ 5269 data_format=data_format, name=name)
5270 _result = _outputs[:]
5271 if _execute.must_record_gradient():
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
742 op = g._create_op_internal(op_type_name, inputs, dtypes=None,
743 name=scope, input_types=input_types,
→ 744 attrs=attr_protos, op_def=op_def)
745
746 # outputs is returned as a separate return value so that the output
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
591 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
592 op_type, inputs, dtypes, input_types, name, attrs, op_def,
→ 593 compute_device)
594
595 def capture(self, tensor, name=None, shape=None):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3483 input_types=input_types,
3484 original_op=self._default_original_op,
→ 3485 op_def=op_def)
3486 self._create_op_helper(ret, compute_device=compute_device)
3487 return ret
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in init(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1973 op_def = self._graph._get_op_def(node_def.op)
1974 self._c_op = _create_c_op(self._graph, node_def, inputs,
→ 1975 control_input_ops, op_def)
1976 name = compat.as_str(node_def.name)
1977 # pylint: enable=protected-access
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs, op_def)
1813 except errors.InvalidArgumentError as e:
1814 # Convert to ValueError for backwards compatibility.
→ 1815 raise ValueError(str(e))
1816
1817 return c_op
ValueError: Shape must be rank 4 but is rank 5 for ‘{{node max_pooling2d_8/MaxPool}} = MaxPoolT=DT_FLOAT, data_format=“NHWC”, ksize=[1, 4, 4, 1], padding=“SAME”, strides=[1, 4, 4, 1]’ with input shapes: [1,?,8,8,16].