Exercise 2 - convolutional_model

Course Q&A Deep Learning Specialization Convolutional Neural Networks
1

Hi
I have written all the commands in accordance with each layer, but I got an error. Additionally, I have searched in discourse, but I couldn’t find the response. I would so appreciate you if you could help me.
Here Is my received error:

ERROR:

TypeError 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)
36 # outputs = None
37 # YOUR CODE STARTS HERE
—> 38 Z1 = tfl.Conv2D(kernel_size=(4,4),filters=8,strides=1,padding=“same”,input_shape=input_img)
39 A1 = tfl.ReLU(Z1)
40 P1 = tfl.MaxPool2D(pool_size = (8,8), strides = 8, padding=“same”)(A1)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py in init(self, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
662 kernel_constraint=constraints.get(kernel_constraint),
663 bias_constraint=constraints.get(bias_constraint),
→ 664 **kwargs)
665
666

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/convolutional.py in init(self, rank, filters, kernel_size, strides, padding, data_format, dilation_rate, groups, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, conv_op, **kwargs)
135 name=name,
136 activity_regularizer=regularizers.get(activity_regularizer),
→ 137 **kwargs)
138 self.rank = rank
139

/opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
→ 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in init(self, trainable, name, dtype, dynamic, **kwargs)
403 else:
404 batch_size = None
→ 405 batch_input_shape = (batch_size,) + tuple(kwargs[‘input_shape’])
406 self._batch_input_shape = batch_input_shape
407

/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in iter(self)
510 if shape[0] is None:
511 raise TypeError(
→ 512 “Cannot iterate over a tensor with unknown first dimension.”)
513 return _TensorIterator(self, shape[0])
514

TypeError: Cannot iterate over a tensor with unknown first dimension.

2

Your code for Z1 is incorrect. You need to pass “input_img” as the function parameter, not as the input_shape= argument.

And I think the calling style you used for the ReLU and flatten layers might be wrong also. Try making those use the same method you used for MaxPool2D and Conv2D

3

And please edit your message and remove the code. Posting your code is not allowed by the Honor Code.

1 Like
4

e.g. this should be A2 = tfl.ReLU()(Z2). You get the idea.

2 Likes
5

Thanks for your helpful soloution. It was right

6

Thank you. I got it.