I have stored a Tensorflow model with the files .meta, .index, checkpoint, and .data-0001. I restore my graph and model using:
model = tf.train.import_meta_graph("models/model.meta")
model.restore(sess, tf.train.latest_checkpoint("models/"))
I restored some variables like weights and bias but I also need to restore the loss function. My model is using nce_loss.
Essentially, I want to get the gradient for my loss function given a certain input where I don't have to redefine the loss variables just call the operation from the restored version. So:
loss = graph.get_operation_by_name("loss")
grads = tf.gradients(loss,loss.inputs)
And here I get the following error message:
File "/tmp/fgsm.py", line 114, in main
grads = tf.gradients(loss,loss.inputs)
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py", line 630, in gradients
gate_gradients, aggregation_method, stop_gradients)
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py", line 675, in _GradientsHelper
ys = ops.convert_n_to_tensor_or_indexed_slices(ys, name="y")
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1377, in convert_n_to_tensor_or_indexed_slices
values=values, dtype=dtype, name=name, as_ref=False)
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1348, in internal_convert_n_to_tensor_or_indexed_slices
value, dtype=dtype, name=n, as_ref=as_ref))
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1307, in internal_convert_to_tensor_or_indexed_slices
value, dtype=dtype, name=name, as_ref=as_ref)
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1146, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/tmp/venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6168, in _operation_conversion_error
name, as_ref))
TypeError: Can't convert Operation 'loss' to Tensor (target dtype=None, name='y_0', as_ref=False)
What am I doing wrong here?
Edit:
so by switching to
loss = graph.get_tensor_by_name("loss:0")
I can successfully get my loss tensor. Now how do I get the gradient for the input given the restored loss function?
nce_loss has an "input" parameter and I want to calculate the gradient given the loss function and the input parameter. How can I use tf.gradients for this? When I do tf.gradients(loss,loss.inputs) I get an error
AttributeError: 'Tensor' object has no attribute 'inputs'
When you are retrieving tensors from tensorflow, you must index them. In your code:
loss = graph.get_operation_by_name("loss")
grads = tf.gradients(loss,loss.inputs)
As the error states you are retrieving the operation of loss not its output tensor. To retrieve its tensor you can index the operation like so:
loss = graph.get_operation_by_name("loss:0")
grads = tf.gradients(loss,loss.inputs)
Related
I try to customize a loss functionm, But when I run the following code:
pressure_grad_x = tf.keras.backend.gradients(out2, cur_x_input)[0]
pressure_grad_y = tf.keras.backend.gradients(out2, cur_y_input)[0]
pressure_grad_z = tf.keras.backend.gradients(out2, cur_z_input)[0]
pressure_grad = tf.convert_to_tensor([pressure_grad_x, pressure_grad_y, pressure_grad_z])
An error will be reported(The above code is in the custom function.)：
<ipython-input-42-23232050871c>:34 call *
pressure_grad = tf.convert_to_tensor([pressure_grad_x, pressure_grad_y, pressure_grad_z])
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\util\dispatch.py:206 wrapper **
return target(*args, **kwargs)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py:1431 convert_to_tensor_v2_with_dispatch
value, dtype=dtype, dtype_hint=dtype_hint, name=name)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py:1441 convert_to_tensor_v2
as_ref=False)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\profiler\trace.py:163 wrapped
return func(*args, **kwargs)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py:1566 convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:346 _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:272 constant
allow_broadcast=True)
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\constant_op.py:290 _constant_impl
allow_broadcast=allow_broadcast))
C:\Users\dell\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\tensor_util.py:553 make_tensor_proto
"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [None, None, None]. Consider casting elements to a supported type.
When I tried to solve it, I found that the value of pressure_grad_x (or pressure_grad_y, pressure_grad_z) is None.
The model i used is LSTM model and take the custom loss function as the last layer of the model.
out2 is the outputs of LSTM model. cur_x_input, cur_y_input, cur_z_input is the inputs of LSTM model.The version of Tensorflow is 2.6.0.
I have no way to solve this problem. I hope someone can help me solve this problem.
I think, you need check you input shape, I feel, your given input is NONE.
Solved via using tf.shape
I was trying to design a multi-class Classification NN.
My train_x dataset contains 23 examples each containing 37 features (dimension : 23*37)
train_y contains output for each example (dimension : 23*7) [ 7 Labels/Classes ]. I used one-hot encoding for each example's output.
len(words) is the number of features
This is my model design :
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(units=len(words), input_shape=[len(words)]),
tf.keras.layers.Dense(8, activation="relu"),
tf.keras.layers.Dense(8, activation="relu"),
tf.keras.layers.Dense(len(labels), activation="softmax")
])
For optimizer I used Adam Optimizer and for loss function I used Sparse Categorical Entropy.
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=['accuracy'])
model.fit(train_x, train_y, epochs=100)
I am getting the following traceback call:
Epoch 1/100
Traceback (most recent call last):
File "main.py", line 83, in <module>
model.fit(train_x, train_y, epochs=100, callbacks=[callbacks])
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 644, in _call
return self._stateless_fn(*args, **kwds)
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 2420, in __call__
return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 1661, in _filtered_call
return self._call_flat(
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 1745, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 593, in call
outputs = execute.execute(
File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [23,7] and labels shape [161]
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at main.py:83) ]] [Op:__inference_train_function_709]
Function call stack:
train_function
I have been searching in various sites for two days. But all of them flattens the input data for first layer. All of them use either grey scale images or RGB images as input. All of them requires the first layer to be flattened. But my input data is already flattened.
As much I am understanding this, I am getting the traceback call for the first layer. I may have misunderstood the concept of units and input_shape, thus defining them incorrectly.
Change sparse_categorical_crossentropy to categorical_crossentropy.
When using custom estimators in Tensorflow 2, when the model contains BatchNorm or Dropout layers, tf fails while building the graph with the following error. It works just fine when I comment out the Dropout and BatchNorm layers.
The model I use is a simple CNN model with two conv blocks and dense layer at the end:
def build_conv_block(x: Model, filter_map_count: int, name: str):
x = Conv2D(filter_map_count, (3, 3), name=f'{name}_conv_2d')(x)
x = BatchNormalization(name=f'{name}_bn')(x) <------- Error when not commented out
x = ReLU(name=f'{name}_relu')(x)
x = MaxPool2D((2, 2), name=f'{name}_max_pool_2d')(x)
x = Dropout(0.25, name=f'{name}_dropout')(x) <------- Error when not commented out
return x
def get_model(params):
input_image = Input(shape=params.input_shape)
x = build_conv_block(input_image, filter_map_count=64, name='layer_1')
x = build_conv_block(x, filter_map_count=128, name='layer_2')
x = Flatten(name='flatten_conv')(x)
output_pred = Dense(10, activation='softmax', name='output')(x)
model = Model(inputs=input_image, outputs=output_pred)
model.optimizer = Adam(learning_rate=params.learning_rate)
return model
I have a standard train_op in the model_fn that takes mnist images and labels as input and the class as output:
# Calculate gradients
with tf.GradientTape() as tape:
y_pred = model(features, training=training)
loss = tf.losses.categorical_crossentropy(labels, y_pred)
if mode == tf.estimator.ModeKeys.TRAIN:
gradients = tape.gradient(loss, model.trainable_variables)
train_op = model.optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
Here's the traceback of the error I get:
Traceback (most recent call last):
File "F:/Projects/python/my_project/train.py", line 38, in <module>
tf.estimator.train_and_evaluate(estimator, train_spec=train_spec, eval_spec=eval_spec)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py", line 473, in train_and_evaluate
return executor.run()
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py", line 613, in run
return self.run_local()
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\training.py", line 714, in run_local
saving_listeners=saving_listeners)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 370, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1160, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1190, in _train_model_default
features, labels, ModeKeys.TRAIN, self.config)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1148, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "F:\Projects\python\my_project\model.py", line 62, in model_fn
gradients = tape.gradient(loss, model.trainable_variables)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\backprop.py", line 1014, in gradient
unconnected_gradients=unconnected_gradients)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\imperative_grad.py", line 76, in imperative_grad
compat.as_str(unconnected_gradients.value))
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\eager\backprop.py", line 138, in _gradient_function
return grad_fn(mock_op, *out_grads)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 120, in _IfGrad
true_graph, grads, util.unique_grad_fn_name(true_graph.name))
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 395, in _create_grad_func
func_graph=_CondGradFuncGraph(name, func_graph))
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 394, in <lambda>
lambda: _grad_fn(func_graph, grads), [], {},
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\cond_v2.py", line 373, in _grad_fn
src_graph=func_graph)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gradients_util.py", line 550, in _GradientsHelper
gradient_uid)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\ops\gradients_util.py", line 175, in _DefaultGradYs
constant_op.constant(1, dtype=y.dtype, name="grad_ys_%d" % i)))
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 227, in constant
allow_broadcast=True)
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 265, in _constant_impl
allow_broadcast=allow_broadcast))
File "F:\Python\envs\tf2\lib\site-packages\tensorflow_core\python\framework\tensor_util.py", line 484, in make_tensor_proto
(dtype, nparray.dtype, values))
TypeError: Incompatible types: <dtype: 'variant'> vs. int32. Value is 1
It looks similar to the error mentioned in TF Issue #31894, but it doesn't seem to solve this problem. The TypeError does not tell much about where and why the error is happening and directly googling it does not help.
Although it may not be too obvious from the TypeError variant vs int32, if we carefully check the logs, we can see that the error occurs when finding gradients:
File "F:\Projects\python\my_project\model.py", line 62, in model_fn
gradients = tape.gradient(loss, model.trainable_variables)
Also, it should be noted that we get the same error even if one of them is present. So, if we try and analyze the common attributes in BatchNormalization and Dropout layer, both may seem to not come under the core layers, but when we look carefully, only those two layers in the model have a different train/test phase i.e. dropout doesn't zero out the values in test phase and batch norm uses a moving mean and variance during test phase.
Now the problem is narrowed down to using any layer that has a different train/test phase. This happens because tensorflow identifies if training mode is on or not using training parameter passed to the model.
This problem can be solved by using
y_pred = model(features, training=True)
when finding the gradients i.e. for the training phase and by using
y_pred = model(features, training=False)
otherwise i.e. for predict and eval phases.
Linked: Errors where moving mean is not updating is also reported, which can be solved by adding the same attribute.
I am using Keras to built a LSTM model.
def LSTM_model_1(X_train,Y_train,Dropout,hidden_units):
model = Sequential()
model.add(Masking(mask_value=666, input_shape=(X_train.shape[1],X_train.shape[2])))
model.add(LSTM(hidden_units, activation='tanh', return_sequences=True, dropout=Dropout))
model.add(LSTM(hidden_units, return_sequences=True))
model.add(LSTM(hidden_units, return_sequences=True))
model.add(Dense(Y_train.shape[-1], activation='softmax'))
model.compile(loss='mean_squared_error', optimizer='adam',metrics['categorical_accuracy'])
return model
The input data is of shape
X_train.shape=(77,100,34); Y_Train.shape=(77,100,7)
The Y data is one-hot-encoded. Both input tensors are zero-padded for the last list entry. The padded values in Y_train is 0. So no state gets a value of 1 for the padded end. dropout=0 and hidden_units=2 which seems not related to the following error.
Unfortunately, I get following error which I think is connected with the shape of Y. But I cannot put my finger on it. The error happens when the first LSTM layer is initialized/added.
ValueError: Initializer for variable lstm_58/kernel/ is from inside a
control-flow construct, such as a loop or conditional. When creating a
variable inside a loop or conditional, use a lambda as the
initializer.
If I follow the error I noticed that it comes down to this:
dtype: If set, initial_value will be converted to the given type.
If None, either the datatype will be kept (if initial_value is
a Tensor), or convert_to_tensor will decide.
"convert to tensor' creates an object which is then None and leads to the error. Apparently, the LSTM tries to convert the input into a tensor... But if I look at my input, it is already a tensor.
Does any of you have an idea what went wrong or how to use lambda as an initializer? Thanks
EDit: the stack trace
File "C:\Users\310122653\Documents\GitHub\DNN\build_model.py", line
44, in LSTM_model_1
model.add(LSTM(hidden_units, activation='tanh', return_sequences=True, dropout=Dropout))
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\models.py",
line 492, in add
output_tensor = layer(self.outputs[0])
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\recurrent.py",
line 499, in call
return super(RNN, self).call(inputs, **kwargs)
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py",
line 592, in call
self.build(input_shapes[0])
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\recurrent.py",
line 461, in build
self.cell.build(step_input_shape)
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\recurrent.py",
line 1838, in build
constraint=self.kernel_constraint)
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\legacy\interfaces.py",
line 91, in wrapper
return func(*args, **kwargs)
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py",
line 416, in add_weight
constraint=constraint)
File
"C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py",
line 395, in variable
v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)
File
"C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py",
line 235, in init
constraint=constraint)
File
"C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py",
line 356, in _init_from_args
"initializer." % name)
The solution, in this case, was to restart the Kernel.
Thanks to Daniel Möller
Is there a way to reshape a TF tensor inside of a custom Keras loss function? I'm defining this custom loss function for a convolutional neural network?
def custom_loss(x, x_hat):
"""
Custom loss function for training background extraction networks (autoencoders)
"""
#flatten x, x_hat before computing mean, median
shape = x_hat.get_shape().as_list()
batch_size = shape[0]
image_size = np.prod(shape[1:])
x = tf.reshape(x, [batch_size, image_size])
x_hat = tf.reshape(x_hat, [batch_size, image_size])
B0 = reduce_median(tf.transpose(x_hat))
# I divide by sigma in the next step. So I add a small float32 to F0
# so as to prevent sigma from becoming 0 or Nan.
F0 = tf.abs(x_hat - B0) + 1e-10
sigma = tf.reduce_mean(tf.sqrt(F0 / 0.5), axis=0)
background_term = tf.reduce_mean(F0 / sigma, axis=-1)
bce = binary_crossentropy(x, x_hat)
loss = bce + background_term
return loss
In addition to computing the standard binary_crossentropy an additional background_term is added into the loss. This term incentives the network to predict images close the median of a batch. Since the outputs of the CNN are 2d and reduce_median works better with 1d arrays I have to reshape the images into 1d arrays. When I try to train this network I get the error
Traceback (most recent call last):
File "stackoverflow.py", line 162, in <module>
autoencoder = build_conv_autoencoder(lambda_W, input_shape, num_filters, optimizer, custom_loss)
File "stackoverflow.py", line 136, in build_conv_autoencoder
autoencoder.compile(optimizer, loss, metrics=[mean_squared_error])
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 594, in compile
**kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 667, in compile
sample_weight, mask)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 318, in weighted
score_array = fn(y_true, y_pred)
File "stackoverflow.py", line 26, in custom_loss
x = tf.reshape(x, [batch_size, image_size])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2448, in reshape
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 494, in apply_op
raise err
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 491, in apply_op
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 710, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in make_tensor_proto
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in <listcomp>
tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
(bytes_or_text,))
TypeError: Expected binary or unicode string, got None
It seems like Keras is calling custom_loss before the TensorFlow graph is instantiated. This makes batch_size None instead of the actual value. Is there a proper way to reshape tensors inside loss functions to this error is avoided? You can look at the full code here .
Is there a proper way to reshape tensors...
If you are using Keras you should use the K.reshape(x,shape) method, which is a wrapper for tf.reshape(x,shape) as we can see in the docs.
I also notice you are using get_shape() to obtain your tensor shape, when on Keras you can do this with K.int_shape(x) as also mentioned in the docs, like this:
shape = K.int_shape(x_hat)
Besides that there are several other operations you do directly calling your Tensorflow import, instead of the Keras Backend (like tf.abs(), tf.reduce_mean(), tf.transpose(), etc.). You should consider using its corresponding wrappers in the keras backend to have uniform notation and guarantee a more regular behaviour. Also, by using the Keras backend you are giving your program compatibility with both Theano and Tensorflow, so it is a big plus you should consider.
Additionally, some TypeError may appear when working with tensors with undefined dimension(s). Please take a look at this question where they explain about reshaping tensors with undefined dimensions. Also, for its equivalent in Keras, check this other question, where in an answer I explain how to achieve that using Keras with Tensorflow as backend.
...Now regarding your code. Basically, as you have some undefined dimensions, you can pass the value -1 to have it infer the shape no matter what size it could be (it is explained in the first linked question, but can also be seen in the docs). Something like:
x = tf.reshape(x, [-1, image_size])
Or using Keras backend:
x = K.reshape(x, [-1, image_size])