probability
probability copied to clipboard
tensorflow
NotImplementedError: Cannot convert a symbolic Tensor (truediv_4577:0) to a numpy array.
Hi, I am trying to train variational autoencoder with reparamaterization trick. In similar implementation on colab, I got error as _SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'latent_sigma/Identity:0' shape=(None, 2) dtype=float32>, <tf.Tensor 'latent_mu/Identity:0' shape=(None, 2) dtype=float32>]
Here is the colab link: https://colab.research.google.com/drive/1_TjoHxDMC3QPQxO9Un1LvMQFHpVAMlMd
Here is my code:
# Definition
i = Input(shape=input_shape, name='encoder_input')
cx = Conv2D(filters=8, kernel_size=3, strides=2, padding='same', activation='relu')(i)
cx = BatchNormalization()(cx)
cx = Conv2D(filters=16, kernel_size=3, strides=2, padding='same', activation='relu')(cx)
cx = BatchNormalization()(cx)
x = Flatten()(cx)
x = Dense(20, activation='relu')(x)
x = BatchNormalization()(x)
mu = Dense(latent_dim, name='latent_mu')(x)
sigma = Dense(latent_dim, name='latent_sigma')(x)
def sample_z(args):
mu, sigma = args
batch = k.shape(mu)[0]
dim = k.shape(mu)[1]
eps = k.random_normal(shape=(batch, dim))
return mu + k.exp(0.5 * sigma) * eps
z = Lambda(sample_z, output_shape=(latent_dim, ), name='z')([mu, sigma])
encoder = Model(i, [mu, sigma, z], name='encoder')
encoder.summary()
conv_shape = K.int_shape(cx)
# Definition
d_i = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(conv_shape[1] * conv_shape[2] * conv_shape[3], activation='relu')(d_i)
x = BatchNormalization()(x)
x = Reshape((conv_shape[1], conv_shape[2], conv_shape[3]))(x)
cx = Conv2DTranspose(filters=16, kernel_size=3, strides=2, padding='same', activation='relu')(x)
cx = BatchNormalization()(cx)
cx = Conv2DTranspose(filters=8, kernel_size=3, strides=2, padding='same', activation='relu')(cx)
cx = BatchNormalization()(cx)
o = Conv2DTranspose(filters=num_channels, kernel_size=3, activation='sigmoid', padding='same', name='decoder_output')(cx)
# Instantiate decoder
decoder = Model(d_i, o, name='decoder')
decoder.summary()
# Instantiate VAE
vae_outputs = decoder(encoder(i)[2])
vae = Model(i, vae_outputs, name='vae')
vae.summary()
opt = tf.keras.optimizers.Adam()
def kl_reconstruction_loss(true, pred):
# Reconstruction loss
reconstruction_loss = binary_crossentropy(K.flatten(true), K.flatten(pred))# * img_width * img_height
kl_loss = tf.constant(0.5) * k.sum(1 + sigma - k.square(mu) - k.exp(sigma), axis=1)
# print(type(kl_loss))
return K.mean(reconstruction_loss + kl_loss)
def step(X, y):
# keep track of our gradients
with tf.GradientTape() as tape:
# make a prediction using the model and then calculate the
# loss
pred = vae(X)
# loss = tf.reduce_mean()
loss = kl_reconstruction_loss(y, pred)
# calculate the gradients using our tape and then update the
# model weights
grads = tape.gradient(loss, vae.trainable_variables)
opt.apply_gradients(zip(grads, vae.trainable_variables))
EPOCHS = 25
BS = 64
numUpdates = int(input_train.shape[0] / BS)
for epoch in range(0, EPOCHS):
# loop over the data in batch size increments
for i in range(0, numUpdates):
start = i * BS
end = start + BS
step(input_train[start:end], input_train[start:end])
print("Done for batch: ", i)
# show timing information for the epoch
vae.compile(optimizer=opt, loss=kl_reconstruction_loss, metrics=["acc"], experimental_run_tf_function=False)
(loss, acc) = vae.evaluate(input_test, input_test)
I got error while running
(loss, acc) = vae.evaluate(input_test, input_test)
And the error is:
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-219-014b7d06d698> in <module>
----> 1 (loss, acc) = vae.evaluate(input_test, input_test)
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing)
928 max_queue_size=max_queue_size,
929 workers=workers,
--> 930 use_multiprocessing=use_multiprocessing)
931
932 def predict(self,
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_generator.py in evaluate(self, model, x, y, batch_size, verbose, sample_weight, steps, callbacks, **kwargs)
818 verbose=verbose,
819 workers=0,
--> 820 callbacks=callbacks)
821
822 def predict(self,
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
293 batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
294 callbacks._call_batch_hook(mode, 'end', step, batch_logs)
--> 295 progbar.on_batch_end(step, batch_logs)
296 step += 1
297
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/keras/callbacks.py in on_batch_end(self, batch, logs)
779 # will be handled by on_epoch_end.
780 if self.verbose and (self.target is None or self.seen < self.target):
--> 781 self.progbar.update(self.seen, self.log_values)
782
783 def on_epoch_end(self, epoch, logs=None):
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/generic_utils.py in update(self, current, values)
557 info += ' - %s:' % k
558 if isinstance(self._values[k], list):
--> 559 avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
560 if abs(avg) > 1e-3:
561 info += ' %.4f' % avg
<__array_function__ internals> in mean(*args, **kwargs)
~/Documents/env_tf/lib/python3.7/site-packages/numpy/core/fromnumeric.py in mean(a, axis, dtype, out, keepdims)
3333
3334 return _methods._mean(a, axis=axis, dtype=dtype,
-> 3335 out=out, **kwargs)
3336
3337
~/Documents/env_tf/lib/python3.7/site-packages/numpy/core/_methods.py in _mean(a, axis, dtype, out, keepdims)
133
134 def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
--> 135 arr = asanyarray(a)
136
137 is_float16_result = False
~/Documents/env_tf/lib/python3.7/site-packages/numpy/core/_asarray.py in asanyarray(a, dtype, order)
136
137 """
--> 138 return array(a, dtype, copy=False, order=order, subok=True)
139
140
~/Documents/env_tf/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in __array__(self)
726 def __array__(self):
727 raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy"
--> 728 " array.".format(self.name))
729
730 def __len__(self):
NotImplementedError: Cannot convert a symbolic Tensor (truediv_4577:0) to a numpy array.
Hi, sorry to add a comment to an old question but did you make any progress on this ? I have a similiar issue. Thanks
