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_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], [].

sir how did you resolve this issue ?