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import matplotlib.pyplot as plt
%matplotlib inline
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras import backend as K
import cv2


    

    




Using TensorFlow backend.

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def imshow(img):
    plt.imshow(cv2.cvtColor(img,cv2.COLOR_GRAY2RGB))


    

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img_src=cv2.imread('test_img.bmp')
plt.imshow(img_src)


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-9611cae8ffbd> in <module>()
      1 img_src=cv2.imread('test_img.bmp')
----> 2 plt.imshow(img_src)

C:\Anaconda3\lib\site-packages\matplotlib\pyplot.py in imshow(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)
   3027                         filternorm=filternorm, filterrad=filterrad,
   3028                         imlim=imlim, resample=resample, url=url, data=data,
-> 3029                         **kwargs)
   3030     finally:
   3031         ax.hold(washold)

C:\Anaconda3\lib\site-packages\matplotlib\__init__.py in inner(ax, *args, **kwargs)
   1816                     warnings.warn(msg % (label_namer, func.__name__),
   1817                                   RuntimeWarning, stacklevel=2)
-> 1818             return func(ax, *args, **kwargs)
   1819         pre_doc = inner.__doc__
   1820         if pre_doc is None:

C:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)
   4920                               resample=resample, **kwargs)
   4921 
-> 4922         im.set_data(X)
   4923         im.set_alpha(alpha)
   4924         if im.get_clip_path() is None:

C:\Anaconda3\lib\site-packages\matplotlib\image.py in set_data(self, A)
    447         if (self._A.dtype != np.uint8 and
    448                 not np.can_cast(self._A.dtype, np.float)):
--> 449             raise TypeError("Image data can not convert to float")
    450 
    451         if (self._A.ndim not in (2, 3) or

TypeError: Image data can not convert to float





    

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img_src=cv2.cvtColor(img_src,cv2.COLOR_BGR2RGB)


    

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img_zip=cv2.resize(img_src,(640,480))


    

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input_shape = (3,128, 128)
model = Sequential()
model.add(Convolution2D(32,128,128,activation='relu',input_shape = input_shape))
# model.add(Convolution2D(64, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
# model.add(Flatten())

model.compile(loss="mean_squared_error",
              optimizer='sgd',
              metrics=['accuracy'])


    

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model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])


    

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