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Commit ff60ae2e authored by Byungheon Jeong's avatar Byungheon Jeong
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differ image entry

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%% Cell type:code id: tags:
``` python
from numpy import loadtxt
from keras.models import load_model
import tensorflow as tf
from keras import backend as K
```
%% Cell type:code id: tags:
``` python
!pwd
```
%% Output
/userdata/kerasData
%% Cell type:code id: tags:
``` python
def f1(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
```
%% Cell type:code id: tags:
``` python
INIT_LR = 1e-4
batch_size = 16
NUM_EPOCHS = 200
image_size=(128, 128)
class_mode="binary"
image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
TestGenerator = image_generator.flow_from_directory(
"/userdata/kerasData/preloaded/flowDirectory/validation",
target_size=image_size,
batch_size=batch_size,
seed=1000,
class_mode=class_mode)
```
%% Output
Found 2781 images belonging to 2 classes.
%% Cell type:code id: tags:
``` python
model_128 = load_model("/userdata/kerasData/output/model/best_model128_128_e-4.h5", custom_objects={"f1":f1})
model_128.summary()
```
%% Output
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
separable_conv2d (SeparableC (None, 128, 128, 16) 211
_________________________________________________________________
activation (Activation) (None, 128, 128, 16) 0
_________________________________________________________________
batch_normalization (BatchNo (None, 128, 128, 16) 64
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0
_________________________________________________________________
separable_conv2d_1 (Separabl (None, 64, 64, 32) 688
_________________________________________________________________
activation_1 (Activation) (None, 64, 64, 32) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 64, 64, 32) 128
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0
_________________________________________________________________
separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400
_________________________________________________________________
activation_2 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736
_________________________________________________________________
activation_3 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dense (Dense) (None, 128) 2097280
_________________________________________________________________
activation_4 (Activation) (None, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 128) 512
_________________________________________________________________
dropout (Dropout) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 16512
_________________________________________________________________
activation_5 (Activation) (None, 128) 0
_________________________________________________________________
batch_normalization_5 (Batch (None, 128) 512
_________________________________________________________________
dropout_1 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 129
_________________________________________________________________
activation_6 (Activation) (None, 1) 0
=================================================================
Total params: 2,123,684
Trainable params: 2,122,820
Non-trainable params: 864
_________________________________________________________________
%% Cell type:code id: tags:
``` python
model_128.evaluate(TestGenerator)
```
%% Output
174/174 [==============================] - 419s 2s/step - loss: 0.7333 - accuracy: 0.5045 - f1: 0.6095 - recall: 0.8166 - precision: 0.5016
[0.7332570552825928,
0.5044947862625122,
0.6094719767570496,
0.8166065216064453,
0.5015521049499512]
%% Cell type:code id: tags:
``` python
INIT_LR = 1e-4
batch_size = 16
NUM_EPOCHS = 200
image_size=(2048, 1536)
class_mode="categorical"
model_e5 = load_model('/userdata/kerasData/best_model_e5.h5', custom_objects={"f1":f1})
model_e5.summary()
image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
testGenerator = image_generator.flow_from_directory(
"/userdata/kerasData/preloaded/flowDirectory/test",
target_size=image_size,
seed=1000,
batch_size=batch_size,
class_mode=class_mode)
```
%% Output
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
separable_conv2d (SeparableC (None, 128, 128, 16) 211
_________________________________________________________________
activation (Activation) (None, 128, 128, 16) 0
_________________________________________________________________
batch_normalization (BatchNo (None, 128, 128, 16) 64
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 64, 64, 16) 0
_________________________________________________________________
separable_conv2d_1 (Separabl (None, 64, 64, 32) 688
_________________________________________________________________
activation_1 (Activation) (None, 64, 64, 32) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 64, 64, 32) 128
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0
_________________________________________________________________
separable_conv2d_2 (Separabl (None, 32, 32, 64) 2400
_________________________________________________________________
activation_2 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
separable_conv2d_3 (Separabl (None, 32, 32, 64) 4736
_________________________________________________________________
activation_3 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dense (Dense) (None, 128) 2097280
_________________________________________________________________
activation_4 (Activation) (None, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 128) 512
_________________________________________________________________
dropout (Dropout) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 16512
_________________________________________________________________
activation_5 (Activation) (None, 128) 0
_________________________________________________________________
batch_normalization_5 (Batch (None, 128) 512
_________________________________________________________________
dropout_1 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 2) 258
_________________________________________________________________
activation_6 (Activation) (None, 2) 0
=================================================================
Total params: 2,123,813
Trainable params: 2,122,949
Non-trainable params: 864
_________________________________________________________________
Found 3485 images belonging to 2 classes.
%% Cell type:code id: tags:
``` python
image_size=(2048, 1536)
class_mode="categorical"
image_generator = tf.keras.preprocessing.image.ImageDataGenerator()
testGenerator = image_generator.flow_from_directory(
"/userdata/kerasData/preloaded/flowDirectory/test",
target_size=image_size,
seed=1000,
batch_size=batch_size,
class_mode=class_mode)
```
%% Output
Found 3485 images belonging to 2 classes.
%% Cell type:code id: tags:
``` python
model_e5.evaluate(testGenerator)
```
%% Output
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-8-6de600d60988> in <module>
----> 1 model_e5.evaluate(testGenerator)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside `run_distribute_coordinator` already.
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
1079 step_num=step):
1080 callbacks.on_test_batch_begin(step)
-> 1081 tmp_logs = test_function(iterator)
1082 # Catch OutOfRangeError for Datasets of unknown size.
1083 # This blocks until the batch has finished executing.
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
642 # Lifting succeeded, so variables are initialized and we can run the
643 # stateless function.
--> 644 return self._stateless_fn(*args, **kwds)
645 else:
646 canon_args, canon_kwds = \
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
2418 with self._lock:
2419 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2420 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2421
2422 @property
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs)
1663 if isinstance(t, (ops.Tensor,
1664 resource_variable_ops.BaseResourceVariable))),
-> 1665 self.captured_inputs)
1666
1667 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1744 # No tape is watching; skip to running the function.
1745 return self._build_call_outputs(self._inference_function.call(
-> 1746 ctx, args, cancellation_manager=cancellation_manager))
1747 forward_backward = self._select_forward_and_backward_functions(
1748 args,
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
596 inputs=args,
597 attrs=attrs,
--> 598 ctx=ctx)
599 else:
600 outputs = execute.execute_with_cancellation(
/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]
[[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]
[[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_39]]
(1) Invalid argument: Incompatible shapes: [16,2] vs. [3072,2]
[[node mul_1 (defined at <ipython-input-2-85a3f15ecf4d>:24) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_test_function_2377]
Errors may have originated from an input operation.
Input Source operations connected to node mul_1:
sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)
Input Source operations connected to node mul_1:
sequential/activation_6/Softmax (defined at <ipython-input-8-6de600d60988>:1)
Function call stack:
test_function -> test_function
%% Cell type:code id: tags:
``` python
```
ImageLoader.ipynb
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Test_Loader.ipynb 0 → 100644
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expTest.ipynb 0 → 100644
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