Here are my code, I can’t solve this my own, please help!
def ResNet50(input_shape = (64, 64, 3), classes = 6):
"""
Stage-wise implementation of the architecture of the popular ResNet50:
CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
-> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> FLATTEN -> DENSE
Arguments:
input_shape -- shape of the images of the dataset
classes -- integer, number of classes
Returns:
model -- a Model() instance in Keras
"""
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(64, (7, 7), strides = (2, 2), kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3)(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], s = 1)
X = identity_block(X, 3, [64, 64, 256])
X = identity_block(X, 3, [64, 64, 256])
### START CODE HERE
## Stage 3 (≈4 lines)
X = convolutional_block(X, f = 3, filters = [128,128,512], s = 2)
X = identity_block(X, 3, [128,128,512])
X = identity_block(X, 3, [128,128,512])
X = identity_block(X, 3, [128,128,512])
## Stage 4 (≈6 lines)
X = convolutional_block(X, f = 3, filters = [256, 256, 1024], s = 2)
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
## Stage 5 (≈3 lines)
X = convolutional_block(X, f = 3, filters = [512, 512, 2048], s = 2)
X = identity_block(X, 3, [512, 512, 2048])
X = identity_block(X, 3, [512, 512, 2048])
## AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
X = AveragePooling2D(pool_size=(2,2))(X)
### END CODE HERE
# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', kernel_initializer = glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs = X_input, outputs = X)
return model```
when I run
model = ResNet50(input_shape = (64, 64, 3), classes = 6)
print(model.summary())
ValueError Traceback (most recent call last)
in
----> 1 model = ResNet50()
2 print(model.summary())
in ResNet50(input_shape, classes)
37
38 ## Stage 3 (≈4 lines)
—> 39 X = convolutional_block(X, f = 3, filters = [128,128,512], s = 2)
40 X = identity_block(X, 3, [128,128,512])
41 X = identity_block(X, 3, [128,128,512])
in convolutional_block(X, f, filters, s, training, initializer)
52
53 # Final step: Add shortcut value to main path (Use this order [X, X_shortcut]), and pass it through a RELU activation
—> 54 X = Add()([X, X_shortcut])
55 X = Activation(‘relu’)(X)
56
/usr/local/lib/python3.6/dist-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.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1096 # Build layer if applicable (if the build method has been
1097 # overridden).
→ 1098 self._maybe_build(inputs)
1099 cast_inputs = self._maybe_cast_inputs(inputs, input_list)
1100
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
2641 # operations.
2642 with tf_utils.maybe_init_scope(self):
→ 2643 self.build(input_shapes) # pylint:disable=not-callable
2644 # We must set also ensure that the layer is marked as built, and the build
2645 # shape is stored since user defined build functions may not be calling
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/tf_utils.py in wrapper(instance, input_shape)
321 if input_shape is not None:
322 input_shape = convert_shapes(input_shape, to_tuples=True)
→ 323 output_shape = fn(instance, input_shape)
324 # Return shapes from fn as TensorShapes.
325 if output_shape is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
110 else:
111 shape = input_shape[i][1:]
→ 112 output_shape = self._compute_elemwise_op_output_shape(output_shape, shape)
113 # If the inputs have different ranks, we have to reshape them
114 # to make them broadcastable.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in _compute_elemwise_op_output_shape(self, shape1, shape2)
83 raise ValueError(
84 'Operands could not be broadcast ’
—> 85 'together with shapes ’ + str(shape1) + ’ ’ + str(shape2))
86 output_shape.append(i)
87 return tuple(output_shape)
ValueError: Operands could not be broadcast together with shapes (8, 8, 512) (4, 4, 512)```