Invalid Argument Error in style_transfer3.py about machine_learning_examples

[kushalj001]

content_image = load_image('./Images/kushal.jpg')
height, width = content_image.shape[1:3]
style_image = load_image('./Images/style.jpg',(height,width))
batch_shape = content_image.shape
shape = content_image.shape[1:]

vgg_model = VGG16_AveragePool(shape)

content_model = VGG16_AveragePool_Cutoff(shape, 11)
content_target = K.variable(content_model.predict(content_image),name='content_target')

conv_outputs = [
layer.get_output_at(1) for layer in vgg_model.layers
if layer.name.endswith('conv1')
]
style_model = Model(vgg_model.input, conv_outputs)
style_outputs = [K.variable(y) for y in style_model.predict(style_image)]
style_weights = [0.2,0.4,0.3,0.5,0.2]

loss = K.mean(K.square(content_model.output - content_target))

for w, symbolic, actual in zip(style_weights,conv_outputs,style_outputs):
loss += w*compute_style_loss(symbolic[0],actual[0])

gradients = K.gradients(loss, vgg_model.input)
get_loss_and_gradients = K.function(inputs=[vgg_model.input],outputs=[loss] + gradients)

def get_loss_and_gradients_wrapper(x):
l,g = get_loss_and_gradients([x.reshape(*batch_shape)])
return l.astype(np.float64),g.flatten().astype(np.float64)

final_image = minimize(get_loss_and_gradients_wrapper,10,batch_shape)
plt.imshow(scale(final_image))
plt.show()

InvalidArgumentError: You must feed a value for placeholder tensor 'sequential_18_input' with dtype float and shape [?,640,640,3]
[[Node: sequential_18_input = Placeholderdtype=DT_FLOAT, shape=[?,640,640,3], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

StackTrace

InvalidArgumentError Traceback (most recent call last)
in ()
114 return l.astype(np.float64),g.flatten().astype(np.float64)
115
--> 116 final_image = minimize(get_loss_and_gradients_wrapper,10,batch_shape)
117 plt.imshow(scale(final_image))
118 plt.show()

in minimize(fn, epochs, batch_shape)
59 x = np.random.randn(np.prod(batch_shape))
60 for i in range(epochs):
---> 61 x, l,_ = fmin_l_bfgs_b(func=fn,x0=x,maxfun=20)
62 x = np.clip(x,-127,127)
63 print("iteration=%s, loss=%s" %(i,l))

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\scipy\optimize\lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
197
198 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
--> 199 **opts)
200 d = {'grad': res['jac'],
201 'task': res['message'],

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\scipy\optimize\lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)
333 # until the completion of the current minimization iteration.
334 # Overwrite f and g:
--> 335 f, g = func_and_grad(x)
336 elif task_str.startswith(b'NEW_X'):
337 # new iteration

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\scipy\optimize\lbfgsb.py in func_and_grad(x)
283 else:
284 def func_and_grad(x):
--> 285 f = fun(x, *args)
286 g = jac(x, *args)
287 return f, g

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\scipy\optimize\optimize.py in function_wrapper(*wrapper_args)
291 def function_wrapper(wrapper_args):
292 ncalls[0] += 1
--> 293 return function((wrapper_args + args))
294
295 return ncalls, function_wrapper

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\scipy\optimize\optimize.py in call(self, x, *args)
61 def call(self, x, *args):
62 self.x = numpy.asarray(x).copy()
---> 63 fg = self.fun(x, *args)
64 self.jac = fg[1]
65 return fg[0]

in get_loss_and_gradients_wrapper(x)
111
112 def get_loss_and_gradients_wrapper(x):
--> 113 l,g = get_loss_and_gradients([x.reshape(*batch_shape)])
114 return l.astype(np.float64),g.flatten().astype(np.float64)
115

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\keras\backend\tensorflow_backend.py in call(self, inputs)
2664 return self._legacy_call(inputs)
2665
-> 2666 return self._call(inputs)
2667 else:
2668 if py_any(is_tensor(x) for x in inputs):

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
2634 symbol_vals,
2635 session)
-> 2636 fetched = self._callable_fn(*array_vals)
2637 return fetched[:len(self.outputs)]
2638

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py in call(self, *args, **kwargs)
1380 ret = tf_session.TF_SessionRunCallable(
1381 self._session._session, self._handle, args, status,
-> 1382 run_metadata_ptr)
1383 if run_metadata:
1384 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

c:\users\kushal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\framework\errors_impl.py in exit(self, type_arg, value_arg, traceback_arg)
517 None, None,
518 compat.as_text(c_api.TF_Message(self.status.status)),
--> 519 c_api.TF_GetCode(self.status.status))
520 # Delete the underlying status object from memory otherwise it stays alive
521 # as there is a reference to status from this from the traceback due to