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WARNING:tensorflow:Layer conv2d is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call tf.keras.backend.set_floatx('float64'). To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

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UnknownError Traceback (most recent call last)
in
----> 1 predictions = model(x_train[:1]).numpy()

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in call(self, inputs, *args, **kwargs)
820 with base_layer_utils.autocast_context_manager(
821 self._compute_dtype):
--> 822 outputs = self.call(cast_inputs, *args, **kwargs)
823 self._handle_activity_regularization(inputs, outputs)
824 self._set_mask_metadata(inputs, outputs, input_masks)

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py in call(self, inputs, training, mask)
265 if not self.built:
266 self._init_graph_network(self.inputs, self.outputs, name=self.name)
--> 267 return super(Sequential, self).call(inputs, training=training, mask=mask)
268
269 outputs = inputs # handle the corner case where self.layers is empty

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py in call(self, inputs, training, mask)
715 return self._run_internal_graph(
716 inputs, training=training, mask=mask,
--> 717 convert_kwargs_to_constants=base_layer_utils.call_context().saving)
718
719 def compute_output_shape(self, input_shape):

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py in _run_internal_graph(self, inputs, training, mask, convert_kwargs_to_constants)
889
890 # Compute outputs.
--> 891 output_tensors = layer(computed_tensors, **kwargs)
892
893 # Update tensor_dict.

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py in call(self, inputs, *args, **kwargs)
820 with base_layer_utils.autocast_context_manager(
821 self._compute_dtype):
--> 822 outputs = self.call(cast_inputs, *args, **kwargs)
823 self._handle_activity_regularization(inputs, outputs)
824 self._set_mask_metadata(inputs, outputs, input_masks)

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py in call(self, inputs)
207 inputs = array_ops.pad(inputs, self._compute_causal_padding())
208
--> 209 outputs = self._convolution_op(inputs, self.kernel)
210
211 if self.use_bias:

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in call(self, inp, filter)
1133 call_from_convolution=False)
1134 else:
-> 1135 return self.conv_op(inp, filter)
1136 # copybara:strip_end
1137 # copybara:insert return self.conv_op(inp, filter)

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in call(self, inp, filter)
638
639 def call(self, inp, filter): # pylint: disable=redefined-builtin
--> 640 return self.call(inp, filter)
641
642

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in call(self, inp, filter)
237 padding=self.padding,
238 data_format=self.data_format,
--> 239 name=self.name)
240
241

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)
2009 data_format=data_format,
2010 dilations=dilations,
-> 2011 name=name)
2012
2013

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
931 input, filter, strides=strides, use_cudnn_on_gpu=use_cudnn_on_gpu,
932 padding=padding, explicit_paddings=explicit_paddings,
--> 933 data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
934 except _core._SymbolicException:
935 pass # Add nodes to the TensorFlow graph.

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py in conv2d_eager_fallback(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name, ctx)
1020 explicit_paddings, "data_format", data_format, "dilations", dilations)
1021 _result = _execute.execute(b"Conv2D", 1, inputs=_inputs_flat, attrs=_attrs,
-> 1022 ctx=ctx, name=name)
1023 if _execute.must_record_gradient():
1024 _execute.record_gradient(

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [

C:\ProgramData\Anaconda3\envs\tf_gpu\lib\site-packages\six.py in raise_from(value, from_value)

UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]