please help in this regard.

[srinivasb72s1]

Sir Really, you have done wonderful work. But there is not available the file weights-full-best .h5. how to overcome this.

[srinivasb72s1]

could you please help in this. how to get these files weights-full-best.h5, weights-core-best.h5, weights-ET-best.h5

[srinivasb72s1]

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer.

InvalidArgumentError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs) 1658 try: -> 1659 c_op = c_api.TF_FinishOperation(op_desc) 1660 except errors.InvalidArgumentError as e:

InvalidArgumentError: Shape must be rank 1 but is rank 0 for 'batch_normalization_1/cond/Reshape_4' (op: 'Reshape') with input shapes: [1,64,1,1], [].

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)

in () ----> 1 model = unet_model() 2 model.load_weights('weights-full-best.h5') 3 #history = model.fit(x, y, batch_size=16, validation_split=0,validation_data = (val_x,val_y) ,epochs = 40,callbacks = callbacks_list ,verbose=1, shuffle=True) in unet_model() 12 inputs = Input((2, img_size, img_size)) 13 conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs) ---> 14 batch1 = BatchNormalization(axis=1)(conv1) 15 conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1) 16 batch1 = BatchNormalization(axis=1)(conv1) /usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs) 455 # Actually call the layer, 456 # collecting output(s), mask(s), and shape(s). --> 457 output = self.call(inputs, **kwargs) 458 output_mask = self.compute_mask(inputs, previous_mask) 459 /usr/local/lib/python3.6/dist-packages/keras/layers/normalization.py in call(self, inputs, training) 204 return K.in_train_phase(normed_training, 205 normalize_inference, --> 206 training=training) 207 208 def get_config(self): /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in in_train_phase(x, alt, training) 3121 3122 # else: assume learning phase is a placeholder tensor. -> 3123 x = switch(training, x, alt) 3124 if uses_learning_phase: 3125 x._uses_learning_phase = True /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in switch(condition, then_expression, else_expression) 3056 x = tf.cond(condition, 3057 then_expression_fn, -> 3058 else_expression_fn) 3059 else: 3060 # tf.where needs its condition tensor /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs) 505 'in a future version' if date is None else ('after %s' % date), 506 instructions) --> 507 return func(*args, **kwargs) 508 509 doc = _add_deprecated_arg_notice_to_docstring( /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py in cond(pred, true_fn, false_fn, strict, name, fn1, fn2) 2106 try: 2107 context_f.Enter() -> 2108 orig_res_f, res_f = context_f.BuildCondBranch(false_fn) 2109 if orig_res_f is None: 2110 raise ValueError("false_fn must have a return value.") /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py in BuildCondBranch(self, fn) 1939 """Add the subgraph defined by fn() to the graph.""" 1940 pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access -> 1941 original_result = fn() 1942 post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access 1943 if len(post_summaries) > len(pre_summaries): /usr/local/lib/python3.6/dist-packages/keras/layers/normalization.py in normalize_inference() 165 broadcast_gamma, 166 axis=self.axis, --> 167 epsilon=self.epsilon) 168 else: 169 return K.batch_normalization( /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in batch_normalization(x, mean, var, beta, gamma, axis, epsilon) 1906 # so it may have extra axes with 1, it is not needed and should be removed 1907 if ndim(mean) > 1: -> 1908 mean = tf.reshape(mean, (-1)) 1909 if ndim(var) > 1: 1910 var = tf.reshape(var, (-1)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in reshape(tensor, shape, name) 7177 try: 7178 _, _, _op = _op_def_lib._apply_op_helper( -> 7179 "Reshape", tensor=tensor, shape=shape, name=name) 7180 except (TypeError, ValueError): 7181 result = _dispatch.dispatch( /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 786 op = g.create_op(op_type_name, inputs, output_types, name=scope, 787 input_types=input_types, attrs=attr_protos, --> 788 op_def=op_def) 789 return output_structure, op_def.is_stateful, op 790 /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs) 505 'in a future version' if date is None else ('after %s' % date), 506 instructions) --> 507 return func(*args, **kwargs) 508 509 doc = _add_deprecated_arg_notice_to_docstring( /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in create_op(***failed resolving arguments***) 3298 input_types=input_types, 3299 original_op=self._default_original_op, -> 3300 op_def=op_def) 3301 self._create_op_helper(ret, compute_device=compute_device) 3302 return ret /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def) 1821 op_def, inputs, node_def.attr) 1822 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs, -> 1823 control_input_ops) 1824 1825 # Initialize self._outputs. /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs) 1660 except errors.InvalidArgumentError as e: 1661 # Convert to ValueError for backwards compatibility. -> 1662 raise ValueError(str(e)) 1663 1664 return c_op ValueError: Shape must be rank 1 but is rank 0 for 'batch_normalization_1/cond/Reshape_4' (op: 'Reshape') with input shapes: [1,64,1,1], [].

[AndyWangON]

had posted the link in readme.

[asoftlabai]

thank you

[hechengen]

@srinivasb72s1 This is a bug in Keras, I reinstall Keras 2.1.6, and this bug disappears.

[srinivasb72s1]

when I am executing following code on brats 2017. I have faced the below error. please help me with this.

training

num = 31100

model = unet_model() history = model.fit(x, y, batch_size=16, validation_split=0.2 ,epochs= num_epoch, verbose=1, shuffle=True) pred = model.predict(x[num:num+100])

ValueError Traceback (most recent call last)

in () 2 3 model = unet_model() ----> 4 history = model.fit(x, y, batch_size=16, validation_split=0.2 ,epochs= num_epoch, verbose=1, shuffle=True) 5 pred = model.predict(x[num:num+100]) 2 frames /usr/local/lib/python3.6/dist-packages/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, **kwargs) 1628 sample_weight=sample_weight, 1629 class_weight=class_weight, -> 1630 batch_size=batch_size) 1631 # Prepare validation data. 1632 do_validation = False /usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size) 1474 self._feed_input_shapes, 1475 check_batch_axis=False, -> 1476 exception_prefix='input') 1477 y = _standardize_input_data(y, self._feed_output_names, 1478 output_shapes, /usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 111 ': expected ' + names[i] + ' to have ' + 112 str(len(shape)) + ' dimensions, but got array ' --> 113 'with shape ' + str(data_shape)) 114 if not check_batch_axis: 115 data_shape = data_shape[1:] ValueError: Error when checking input: expected input_7 to have 4 dimensions, but got array with shape (0, 1)

[srinivasb72s1]

@polo8214, thank you so much sir.

[srinivasb72s1]

@hechengen, thank you sir

[srinivasb72s1]

please help me in training the model.

[srinivasb72s1]

is there any alternative to find the values of dice coeff and loss, without a history of the model (training).

[Jithinpandu]

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.

Instructions for updating: Colocations handled automatically by placer. InvalidArgumentError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs) 1658 try: -> 1659 c_op = c_api.TF_FinishOperation(op_desc) 1660 except errors.InvalidArgumentError as e:

InvalidArgumentError: Shape must be rank 1 but is rank 0 for 'batch_normalization_1/cond/Reshape_4' (op: 'Reshape') with input shapes: [1,64,1,1], [].

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) in () ----> 1 model = unet_model() 2 model.load_weights('weights-full-best.h5') 3 #history = model.fit(x, y, batch_size=16, validation_split=0,validation_data = (val_x,val_y) ,epochs = 40,callbacks = callbacks_list ,verbose=1, shuffle=True)

in unet_model() 12 inputs = Input((2, img_size, img_size)) 13 conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs) ---> 14 batch1 = BatchNormalization(axis=1)(conv1) 15 conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1) 16 batch1 = BatchNormalization(axis=1)(conv1)

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in call(self, inputs, kwargs) 455 # Actually call the layer, 456 # collecting output(s), mask(s), and shape(s). --> 457 output = self.call(inputs, kwargs) 458 output_mask = self.compute_mask(inputs, previous_mask) 459

/usr/local/lib/python3.6/dist-packages/keras/layers/normalization.py in call(self, inputs, training) 204 return K.in_train_phase(normed_training, 205 normalize_inference, --> 206 training=training) 207 208 def get_config(self):

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in in_train_phase(x, alt, training) 3121 3122 # else: assume learning phase is a placeholder tensor. -> 3123 x = switch(training, x, alt) 3124 if uses_learning_phase: 3125 x._uses_learning_phase = True

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in switch(condition, then_expression, else_expression) 3056 x = tf.cond(condition, 3057 then_expression_fn, -> 3058 else_expression_fn) 3059 else: 3060 # tf.where needs its condition tensor

/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, *kwargs) 505 'in a future version' if date is None else ('after %s' % date), 506 instructions) --> 507 return func(args, **kwargs) 508 509 doc = _add_deprecated_arg_notice_to_docstring(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py in cond(pred, true_fn, false_fn, strict, name, fn1, fn2) 2106 try: 2107 context_f.Enter() -> 2108 orig_res_f, res_f = context_f.BuildCondBranch(false_fn) 2109 if orig_res_f is None: 2110 raise ValueError("false_fn must have a return value.")

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py in BuildCondBranch(self, fn) 1939 """Add the subgraph defined by fn() to the graph.""" 1940 pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access -> 1941 original_result = fn() 1942 post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access 1943 if len(post_summaries) > len(pre_summaries):

/usr/local/lib/python3.6/dist-packages/keras/layers/normalization.py in normalize_inference() 165 broadcast_gamma, 166 axis=self.axis, --> 167 epsilon=self.epsilon) 168 else: 169 return K.batch_normalization(

/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in batch_normalization(x, mean, var, beta, gamma, axis, epsilon) 1906 # so it may have extra axes with 1, it is not needed and should be removed 1907 if ndim(mean) > 1: -> 1908 mean = tf.reshape(mean, (-1)) 1909 if ndim(var) > 1: 1910 var = tf.reshape(var, (-1))

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_arrayops.py in reshape(tensor, shape, name) 7177 try: 7178 , _, _op = _op_def_lib._apply_op_helper( -> 7179 "Reshape", tensor=tensor, shape=shape, name=name) 7180 except (TypeError, ValueError): 7181 result = _dispatch.dispatch(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords) 786 op = g.create_op(op_type_name, inputs, output_types, name=scope, 787 input_types=input_types, attrs=attr_protos, --> 788 op_def=op_def) 789 return output_structure, op_def.is_stateful, op 790

/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py in new_func(*args, *kwargs) 505 'in a future version' if date is None else ('after %s' % date), 506 instructions) --> 507 return func(args, **kwargs) 508 509 doc = _add_deprecated_arg_notice_to_docstring(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in createop(failed resolving arguments_) 3298 input_types=input_types, 3299 original_op=self._default_original_op, -> 3300 op_def=op_def) 3301 self._create_op_helper(ret, compute_device=compute_device) 3302 return ret

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in init(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def) 1821 op_def, inputs, node_def.attr) 1822 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs, -> 1823 control_input_ops) 1824 1825 # Initialize self._outputs.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs) 1660 except errors.InvalidArgumentError as e: 1661 # Convert to ValueError for backwards compatibility. -> 1662 raise ValueError(str(e)) 1663 1664 return c_op

ValueError: Shape must be rank 1 but is rank 0 for 'batch_normalization_1/cond/Reshape_4' (op: 'Reshape') with input shapes: [1,64,1,1], [].

sir how did you resolve this issue ?