anujshah1003 / own_data_cnn_implementation_keras Goto Github PK

score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)
Traceback (most recent call last):

File "", line 1, in
score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)

TypeError: evaluate() got an unexpected keyword argument 'show_accuracy'

While executing the code i am getting the above error in the following code.:

model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)

error as:

ValueError Traceback (most recent call last)
in ()
27 epochs=epochs,
28 validation_data=validation_generator,
---> 29 validation_steps=nb_validation_samples // batch_size)
30
31 model.save_weights('first_try.h5')

~\AppData\Roaming\Python\Python36\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your ' + object_name + 87 ' call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper

~\AppData\Roaming\Python\Python36\site-packages\keras\models.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1122 workers=workers,
1123 pickle_safe=pickle_safe,
-> 1124 initial_epoch=initial_epoch)
1125
1126 @interfaces.legacy_generator_methods_support

~\AppData\Roaming\Python\Python36\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your ' + object_name + 87 ' call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper

~\AppData\Roaming\Python\Python36\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1922 max_q_size=max_q_size,
1923 workers=workers,
-> 1924 pickle_safe=pickle_safe)
1925 else:
1926 # No need for try/except because

~\AppData\Roaming\Python\Python36\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your ' + object_name + 87 ' call to the Keras 2 API: ' + signature, stacklevel=2)
---> 88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper

~\AppData\Roaming\Python\Python36\site-packages\keras\engine\training.py in evaluate_generator(self, generator, steps, max_q_size, workers, pickle_safe)
2008 '(x, y, sample_weight) '
2009 'or (x, y). Found: ' +
-> 2010 str(generator_output))
2011 if len(generator_output) == 2:
2012 x, y = generator_output

ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None

ValueError: Error when checking input: expected conv2d_59_input to have 4 dimensions,
but got an array with shape (88, 128, 128)
Things look fine to me, not sure what's wrong.

My train accuracy is as high as 93%, test accuracy is not even 60%

Train on 646 samples, validate on 162 samples
Epoch 1/20
646/646 [==============================] - 3s 5ms/step - loss: 1.6475 - acc: 0.2771 - val_loss: 1.3762 - val_acc: 0.4012
Epoch 2/20
646/646 [==============================] - 1s 2ms/step - loss: 1.3441 - acc: 0.3746 - val_loss: 1.3336 - val_acc: 0.3951
Epoch 3/20
646/646 [==============================] - 1s 2ms/step - loss: 1.2240 - acc: 0.4628 - val_loss: 1.2016 - val_acc: 0.4506
Epoch 4/20
646/646 [==============================] - 1s 2ms/step - loss: 1.0834 - acc: 0.5325 - val_loss: 1.1964 - val_acc: 0.4012
Epoch 5/20
646/646 [==============================] - 1s 2ms/step - loss: 1.0213 - acc: 0.5666 - val_loss: 1.1028 - val_acc: 0.4321
Epoch 6/20
646/646 [==============================] - 1s 2ms/step - loss: 0.8154 - acc: 0.6625 - val_loss: 1.0602 - val_acc: 0.5864
Epoch 7/20
646/646 [==============================] - 1s 2ms/step - loss: 0.7496 - acc: 0.7198 - val_loss: 1.0646 - val_acc: 0.5370
Epoch 8/20
646/646 [==============================] - 1s 2ms/step - loss: 0.6410 - acc: 0.7554 - val_loss: 1.0732 - val_acc: 0.5123
Epoch 9/20
646/646 [==============================] - 1s 2ms/step - loss: 0.5153 - acc: 0.8204 - val_loss: 1.2143 - val_acc: 0.5617
Epoch 10/20
646/646 [==============================] - 1s 2ms/step - loss: 0.4199 - acc: 0.8452 - val_loss: 1.2830 - val_acc: 0.5617
Epoch 11/20
646/646 [==============================] - 1s 2ms/step - loss: 0.3557 - acc: 0.8607 - val_loss: 1.3736 - val_acc: 0.5679
Epoch 12/20
646/646 [==============================] - 1s 2ms/step - loss: 0.3054 - acc: 0.8947 - val_loss: 1.3489 - val_acc: 0.5617
Epoch 13/20
646/646 [==============================] - 1s 2ms/step - loss: 0.2247 - acc: 0.9257 - val_loss: 1.6261 - val_acc: 0.5864
Epoch 14/20
646/646 [==============================] - 1s 2ms/step - loss: 0.2627 - acc: 0.9164 - val_loss: 1.3588 - val_acc: 0.5864
Epoch 15/20
646/646 [==============================] - 1s 2ms/step - loss: 0.2434 - acc: 0.9133 - val_loss: 1.5136 - val_acc: 0.5864
Epoch 16/20
646/646 [==============================] - 1s 2ms/step - loss: 0.1817 - acc: 0.9427 - val_loss: 1.8481 - val_acc: 0.6235
Epoch 17/20
646/646 [==============================] - 1s 2ms/step - loss: 0.1826 - acc: 0.9427 - val_loss: 1.6952 - val_acc: 0.6049
Epoch 18/20
646/646 [==============================] - 1s 2ms/step - loss: 0.1354 - acc: 0.9567 - val_loss: 2.2561 - val_acc: 0.5926
Epoch 19/20
646/646 [==============================] - 1s 2ms/step - loss: 0.1587 - acc: 0.9365 - val_loss: 2.0255 - val_acc: 0.5864
Epoch 20/20
646/646 [==============================] - 1s 2ms/step - loss: 0.1892 - acc: 0.9396 - val_loss: 2.1586 - val_acc: 0.5432

Now test
('Test Loss:', 2.15857571731379)
('Test accuracy:', 0.5432098765432098)

How do implement this code on a GPU using tensorflow backend

Hello,
In read me file there is dead link to part 1 of tutorial.
I guess it should be: https://www.youtube.com/watch?v=u8BW_fl6WRc
Have a nice day.

Thanks for the video tutorial.
I think the way the labels are assigned is problematic. The "os.listdir(data_path + '/' + dataset)" doesn't seems to list the directories alphabetically in ascending order, if you print the list you will see the directories are read as ['cats', 'horses', 'dogs', 'humans']. Assigning the labels in ascending order (as in names = ['cats', 'dogs' 'horses', 'humans']) and matching them to the read data in the directories (as labels[0:202] =0 for cat and [202:404] = 2 for dogs then [202:404]=1 for dogs would actually contain horses) would likely to create mismatch between the assigned labels and the actual labels.
The way around this is to sort img_list before you read the images.

Hi Friend,

Can you help me to check this issue?

Traceback (most recent call last):
File "D:/projects-python/keras-sample/cnn/custom_data_cnn.py", line 217, in
plt.plot(xc,train_loss)
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\pyplot.py", line 3317, in plot
ret = ax.plot(*args, **kwargs)
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib_init_.py", line 1898, in inner
return func(ax, *args, **kwargs)
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\axes_axes.py", line 1406, in plot
for line in self._get_lines(*args, **kwargs):
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\axes_base.py", line 407, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\axes_base.py", line 385, in _plot_args
x, y = self._xy_from_xy(x, y)
File "C:\Users\ccc\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\axes_base.py", line 244, in _xy_from_xy
"have shapes {} and {}".format(x.shape, y.shape))
ValueError: x and y must have same first dimension, but have shapes (20,) and (2,)

Hi
Thanks for your job

I notice that ligne 12 sklearn.cross_validation is not anymore implemented
You have to use sklearn.model_selection
Regards

Kindly explain your answer. I did this but I am not sure if it's correct.

PATH = os.getcwd()
# Define data path
data_path2 =  r'C:\Users\Admin\test'
data_dir_list_t = os.listdir(data_path2)

img_rows=64
img_cols=64
num_channel=3
num_epoch= 40

# Define the number of classes
num_classes = 2

img_data_list_t=[]

for dataset in data_dir_list_t:
	img_list_t=os.listdir(data_path2 +'/'+ dataset)
	print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
	for img in img_list_t:
		input_img_t=cv2.imread(data_path2 + '/'+ dataset + '/'+ img )
		#input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
		input_img_t_resize=cv2.resize(input_img,(64,64))
		img_data_list_t.append(input_img_t_resize)

img_data1 = np.array(img_data_list_t)
img_data1 = img_data1.astype('float32')
img_data1 /= 255
print (img_data1.shape)

if num_channel==1:
	if K.image_dim_ordering()=='th':
		img_data1= np.expand_dims(img_data1, axis=1) 
		print (img_data1.shape)
	else:
		img_data1= np.expand_dims(img_data1, axis=4) 
		print (img_data1.shape)
		
else:
	if K.image_dim_ordering()=='th':
		img_data1=np.rollaxis(img_data1,3,1)
		print (img_data1.shape)
		
#%%
USE_SKLEARN_PREPROCESSING=False

if USE_SKLEARN_PREPROCESSING:
	# using sklearn for preprocessing
	from sklearn import preprocessing
	
	def image_to_feature_vector_t(image, size=(64,64)):
		# resize the image to a fixed size, then flatten the image into
		# a list of raw pixel intensities
		return cv2.resize(image, size).flatten()
	
	img_data_list_t=[]
	for dataset in data_dir_list:
		img_list_t=os.listdir(data_path2 +'/'+ dataset)
		print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
		for img in img_list_t:
			input_img=cv2.imread(data_path2 + '/'+ dataset + '/'+ img )
			#input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
			input_img_flatten=image_to_feature_vector_t(input_img,(64,64))
			img_data_list_t.append(input_img_flatten)
	
	img_data1 = np.array(img_data_list_t)
	img_data1 = img_data1.astype('float32')
	print (img_data1.shape)
	img_data1_scaled = preprocessing.scale(img_data1)
	print (img_data1_scaled.shape)
	
	print (np.mean(img_data1_scaled))
	print (np.std(img_data1_scaled))
	
	print (img_data1_scaled.mean(axis=0))
	print (img_data1_scaled.std(axis=0))
	
	if K.image_dim_ordering()=='th':
		img_data1_scaled=img_data1_scaled.reshape(img_data1.shape[0],num_channel,img_rows,img_cols)
		print (img_data1_scaled.shape)
		
	else:
		img_data1_scaled=img_data1_scaled.reshape(img_data1.shape[0],img_rows,img_cols,num_channel)
		print (img_data1_scaled.shape)
	
	
	if K.image_dim_ordering()=='th':
		img_data1_scaled=img_data1_scaled.reshape(img_data1.shape[0],num_channel,img_rows,img_cols)
		print (img_data1_scaled.shape)
		
	else:
		img_data1_scaled=img_data1_scaled.reshape(img_data1.shape[0],img_rows,img_cols,num_channel)
		print (img_data1_scaled.shape)

if USE_SKLEARN_PREPROCESSING:
	img_data1=img_data1_scaled
#%%
# Assigning Labels

# Define the number of classes
num_classes_t = 2

num_of_samples = img_data1.shape[0]
labels_t = np.ones((num_of_samples,),dtype='int64')

labels_t[0:40]=0
labels_t[40:]=1
#labels[404:606]=2
#labels[606:]=3
	  
names = ['cats',' dogs']
	  
# convert class labels to on-hot encoding
test_lbs = np_utils.to_categorical(labels_t, num_classes_t)

#Shuffle the dataset
x_test_t, y_test_t = shuffle(img_data1, test_lbs, random_state=2)
# Split the dataset
#X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)


#%%

# Evaluating the model

score = model.evaluate(x_test_t, y_test_t, batch_size=10)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])

test_image = X_test[0:1]
print (test_image.shape)```...

Re: custom_data_cnn.py
Anaconda2-5.1.0-MacOSX-x86_64.sh
Dear Anuj Shah:
Suspect ‘show_accuracy’ is not supported on this build, please enlighten.
Sincerely,
eltoneo

File "/Users/dutch/anaconda3/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "/Users/dutch/anaconda3/SRC/custom_data_cnn.py", line 246, in
score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)

TypeError: evaluate() got an unexpected keyword argument 'show_accuracy'

/Users/dutch/anaconda3/SRC/custom_data_cnn.py(246)()
244 # Evaluating the model 245 --> 246 score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0) 247 print('Test Loss:', score[0]) 248 print('Test accuracy:', score[1])

when runnig the following line (line 59 in the updated version of the code):
img_data= np.expand_dims(img_data, axis=4) I get the following error:

AxisError: axis 4 is out of bounds for array of dimension 4

Is there somehting changed in the packages? Maybe the function works differently. It also throws the same error if I use it with the exact same data that you provide and apply your code 1:1....

Any help would be much appreciated!