vgg = Vgg16() error

[chenjun0210]

#import Vgg16 helper class

vgg = Vgg16()

---

ValueError Traceback (most recent call last)
in ()
1 #import Vgg16 helper class
----> 2 vgg = Vgg16()

/home/chenjun/Projects/courses/deeplearning1/nbs/vgg16.pyc in init(self)
31 def init(self):
32 self.FILE_PATH = 'models/'
---> 33 self.create()
34 self.get_classes()
35

/home/chenjun/Projects/courses/deeplearning1/nbs/vgg16.pyc in create(self)
69
70 self.ConvBlock(2, 64)
---> 71 self.ConvBlock(2, 128)
72 self.ConvBlock(3, 256)
73 self.ConvBlock(3, 512)

/home/chenjun/Projects/courses/deeplearning1/nbs/vgg16.pyc in ConvBlock(self, layers, filters)
55 model.add(ZeroPadding2D((1, 1)))
56 model.add(Convolution2D(filters, 3, 3, activation='relu'))
---> 57 model.add(MaxPooling2D((2, 2), strides=(2, 2)))
58
59

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/models.pyc in add(self, layer)
325 output_shapes=[self.outputs[0]._keras_shape])
326 else:
--> 327 output_tensor = layer(self.outputs[0])
328 if isinstance(output_tensor, list):
329 raise TypeError('All layers in a Sequential model '

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc in call(self, x, mask)
567 if inbound_layers:
568 # This will call layer.build() if necessary.
--> 569 self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
570 # Outputs were already computed when calling self.add_inbound_node.
571 outputs = self.inbound_nodes[-1].output_tensors

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc in add_inbound_node(self, inbound_layers, node_indices, tensor_indices)
630 # creating the node automatically updates self.inbound_nodes
631 # as well as outbound_nodes on inbound layers.
--> 632 Node.create_node(self, inbound_layers, node_indices, tensor_indices)
633
634 def get_output_shape_for(self, input_shape):

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/engine/topology.pyc in create_node(cls, outbound_layer, inbound_layers, node_indices, tensor_indices)
162
163 if len(input_tensors) == 1:
--> 164 output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
165 output_masks = to_list(outbound_layer.compute_mask(input_tensors[0], input_masks[0]))
166 # TODO: try to auto-infer shape

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/layers/pooling.pyc in call(self, x, mask)
158 strides=self.strides,
159 border_mode=self.border_mode,
--> 160 dim_ordering=self.dim_ordering)
161 return output
162

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/layers/pooling.pyc in _pooling_function(self, inputs, pool_size, strides, border_mode, dim_ordering)
208 output = K.pool2d(inputs, pool_size, strides,
209 border_mode, dim_ordering,
--> 210 pool_mode='max')
211 return output
212

/home/chenjun/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in pool2d(x, pool_size, strides, border_mode, dim_ordering, pool_mode)
2336
2337 if pool_mode == 'max':
-> 2338 x = tf.nn.max_pool(x, pool_size, strides, padding=padding)
2339 elif pool_mode == 'avg':
2340 x = tf.nn.avg_pool(x, pool_size, strides, padding=padding)

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.pyc in max_pool(value, ksize, strides, padding, data_format, name)
687 padding=padding,
688 data_format=data_format,
--> 689 name=name)
690
691

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.pyc in _max_pool(input, ksize, strides, padding, data_format, name)
1121 result = _op_def_lib.apply_op("MaxPool", input=input, ksize=ksize,
1122 strides=strides, padding=padding,
-> 1123 data_format=data_format, name=name)
1124 return result
1125

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords)
701 op = g.create_op(op_type_name, inputs, output_types, name=scope,
702 input_types=input_types, attrs=attr_protos,
--> 703 op_def=op_def)
704 outputs = op.outputs
705 return _Restructure(ops.convert_n_to_tensor(outputs),

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
2334 original_op=self._default_original_op, op_def=op_def)
2335 if compute_shapes:
-> 2336 set_shapes_for_outputs(ret)
2337 self._add_op(ret)
2338 self._record_op_seen_by_control_dependencies(ret)

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in set_shapes_for_outputs(op)
1723 raise RuntimeError("No shape function registered for standard op: %s"
1724 % op.type)
-> 1725 shapes = shape_func(op)
1726 if shapes is None:
1727 raise RuntimeError(

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.pyc in max_pool_shape(op)
508 out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, ksize_r,
509 ksize_c, stride_r, stride_c,
--> 510 padding)
511 output_shape = [batch_size, out_rows, out_cols, depth]
512 else:

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.pyc in get2d_conv_output_size(input_height, input_width, filter_height, filter_width, row_stride, col_stride, padding_type)
187 return get_conv_output_size((input_height, input_width),
188 (filter_height, filter_width),
--> 189 (row_stride, col_stride), padding_type)
190
191

/home/chenjun/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.pyc in get_conv_output_size(input_size, filter_size, strides, padding_type)
152 zip(filter_size, input_size)):
153 raise ValueError("Filter must not be larger than the input: "
--> 154 "Filter: %r Input: %r" % (filter_size, input_size))
155
156 if padding_type == b"VALID":

ValueError: Filter must not be larger than the input: Filter: (2, 2) Input: (1, 112)

[mo-hittudiv]

+1
Same error for me

[racheltho]

It's better to ask this sort of question on the forums