ValueError: Floating point image RGB values must be in the 0..1 range.
ValueError: Floating point image RGB values must be in the 0..1 range.
Time taken for epoch 1 is 132.77519750595093 sec
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py in __call__(self, obj) 332 pass 333 else: --> 334 return printer(obj) 335 # Finally look for special method names 336 method = get_real_method(obj, self.print_method) /usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py in <lambda>(fig) 239 240 if 'png' in formats: --> 241 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 242 if 'retina' in formats or 'png2x' in formats: 243 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) /usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 123 124 bytes_io = BytesIO() --> 125 fig.canvas.print_figure(bytes_io, **kw) 126 data = bytes_io.getvalue() 127 if fmt == 'svg': /usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2214 orientation=orientation, 2215 dryrun=True, -> 2216 **kwargs) 2217 renderer = self.figure._cachedRenderer 2218 bbox_inches = self.figure.get_tightbbox(renderer) /usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi /usr/local/lib/python3.6/dist-packages/matplotlib/backends/backend_agg.py in draw(self) 428 # if toolbar: 429 # toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 # if toolbar: /usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: /usr/local/lib/python3.6/dist-packages/matplotlib/figure.py in draw(self, renderer) 1297 1298 mimage._draw_list_compositing_images( -> 1299 renderer, self, artists, self.suppressComposite) 1300 1301 renderer.close_group('figure') /usr/local/lib/python3.6/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together /usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: /usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe) 2435 renderer.stop_rasterizing() 2436 -> 2437 mimage._draw_list_compositing_images(renderer, self, artists) 2438 2439 renderer.close_group('axes') /usr/local/lib/python3.6/dist-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together /usr/local/lib/python3.6/dist-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: /usr/local/lib/python3.6/dist-packages/matplotlib/image.py in draw(self, renderer, *args, **kwargs) 564 else: 565 im, l, b, trans = self.make_image( --> 566 renderer, renderer.get_image_magnification()) 567 if im is not None: 568 renderer.draw_image(gc, l, b, im) /usr/local/lib/python3.6/dist-packages/matplotlib/image.py in make_image(self, renderer, magnification, unsampled) 791 return self._make_image( 792 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 793 unsampled=unsampled) 794 795 def _check_unsampled_image(self, renderer): /usr/local/lib/python3.6/dist-packages/matplotlib/image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 482 # (of int or float) 483 # or an RGBA array of re-sampled input --> 484 output = self.to_rgba(output, bytes=True, norm=False) 485 # output is now a correctly sized RGBA array of uint8 486 /usr/local/lib/python3.6/dist-packages/matplotlib/cm.py in to_rgba(self, x, alpha, bytes, norm) 255 if xx.dtype.kind == 'f': 256 if norm and xx.max() > 1 or xx.min() < 0: --> 257 raise ValueError("Floating point image RGB values " 258 "must be in the 0..1 range.") 259 if bytes: ValueError: Floating point image RGB values must be in the 0..1 range. <matplotlib.figure.Figure at 0x7f7e6f384908> Time taken for epoch 1 is 132.77519750595093 sec --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-48-d152560ca122> in <module>() ----> 1 train(train_dataset, EPOCHS) <ipython-input-47-24e63cd58368> in train(dataset, epochs) 17 generator.variables) 18 discriminator_gradients = disc_tape.gradient(disc_loss, ---> 19 discriminator.variables) 20 21 generator_optimizer.apply_gradients(zip(generator_gradients, /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/backprop.py in gradient(self, target, sources, output_gradients) 899 nest.flatten(target), 900 flat_sources, --> 901 output_gradients=output_gradients) 902 903 if not self._persistent: /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/imperative_grad.py in imperative_grad(tape, target, sources, output_gradients) 62 target, 63 sources, ---> 64 output_gradients) /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/backprop.py in _gradient_function(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads) 115 return [None] * num_inputs 116 --> 117 return grad_fn(mock_op, *out_grads) 118 119 /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py in _SelectGrad(op, grad) 1115 x = op.inputs[1] 1116 zeros = array_ops.zeros_like(x) -> 1117 return (None, array_ops.where(c, grad, zeros), array_ops.where( 1118 c, zeros, grad)) 1119 /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py in where(condition, x, y, name) 2622 return gen_array_ops.where(condition=condition, name=name) 2623 elif x is not None and y is not None: -> 2624 return gen_math_ops.select(condition=condition, x=x, y=y, name=name) 2625 else: 2626 raise ValueError("x and y must both be non-None or both be None.") /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py in select(condition, x, y, name) 7008 _result = _pywrap_tensorflow.TFE_Py_FastPathExecute( 7009 _ctx._context_handle, _ctx._eager_context.device_name, "Select", name, -> 7010 _ctx._post_execution_callbacks, condition, x, y) 7011 return _result 7012 except _core._FallbackException: KeyboardInterrupt: