I am attempting to build a sequential model in Keras and use it to do image classification. I am getting the following error in the Anaconda3 (Python 3.9) terminal and haven't been able to track down the cause:
(tf) >python image_classification.py 10
Building Sequential Model ...
Compiling ...
Fitting Sequential Model with (X_train, Y_train) ...
Epoch 1/10
Traceback (most recent call last):
File "C:\%\image_classification.py", line 121, in <module>
high_level_neural_net(X_train, Y_train, X_test, Y_test, M, Nnodes1, Nnodes2, inject_layer, af, MAKE_PLOTS)
File "C:\%\image_classification.py", line 67, in high_level_neural_net
model.fit(x=X_train, y=Y_train, epochs=M)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\%\AppData\Local\Temp\__autograph_generated_filev98m50s2.py", line 15, in tf__train_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
TypeError: in user code:
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\training.py", line 1051, in train_function *
return step_function(self, iterator)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\training.py", line 894, in train_step
return self.compute_metrics(x, y, y_pred, sample_weight)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\training.py", line 987, in compute_metrics
self.compiled_metrics.update_state(y, y_pred, sample_weight)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\compile_utils.py", line 480, in update_state
self.build(y_pred, y_true)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\compile_utils.py", line 393, in build
self._metrics = tf.__internal__.nest.map_structure_up_to(
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\compile_utils.py", line 526, in _get_metric_objects
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\compile_utils.py", line 526, in <listcomp>
return [self._get_metric_object(m, y_t, y_p) for m in metrics]
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\engine\compile_utils.py", line 545, in _get_metric_object
metric_obj = metrics_mod.get(metric)
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\metrics\__init__.py", line 182, in get
return deserialize(str(identifier))
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\metrics\__init__.py", line 138, in deserialize
return deserialize_keras_object(
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\utils\generic_utils.py", line 718, in deserialize_keras_object
return obj()
File "C:\%\Anaconda3\envs\tf\lib\site-packages\keras\dtensor\utils.py", line 141, in _wrap_function
init_method(instance, *args, **kwargs)
TypeError: __init__() missing 1 required positional argument: 'normalizer'
So far, I haven't seen the 'normalizer' argument mentioned in the Tensorflow/Keras documentation or on the stackoverflow forums. Here is my source code:
import os
import sys
import math
import scipy as sp
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
def get_dataset():
SHOW_IMAGE = False
mnist = tf.keras.datasets.mnist # 28x28 images of hand-written digits 0-9
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
X_train = tf.keras.utils.normalize(X_train,axis=1) # Normalize input datasets between 0 and 1, Helps the NN converge.
X_test = tf.keras.utils.normalize(X_test,axis=1)
if (SHOW_IMAGE):
plt.imshow(X_train[0])
plt.show()
input()
plt.imshow(Y_train[0])
plt.show()
input()
return X_train, Y_train, X_test, Y_test
def high_level_neural_net(X_train, Y_train, X_test, Y_test, M, Nnodes1, Nnodes2, inject_layer, af, MAKE_PLOTS):
class InjectInputCallback(tf.keras.callbacks.Callback):
# Inject input data to layer between residual blocks; I'm unsure if this works yet.
def __init__(self,train_dataset,layer,logs=None):
self.first_trainds = train_dataset
self.inject_layer = layer
def on_layer_end(self,layer,logs=None):
model.layers[self.inject_layer].output = model.layers[self.inject_layer].output self.first_trainds
print('\n')
print('Building Sequential Model ...\n')
model = Sequential()
model.add(Flatten())
model.add(Dense(Nnodes2, activation=tf.nn.relu)) # Residual block 1 (hidden layers)
model.add(Dense(Nnodes2, activation=tf.nn.relu)) # ...
model.add(Dense(10, activation=tf.nn.relu)) # Output layer of residual block 1/Input layer of residual block 2
model.add(Dense(Nnodes2, activation=tf.nn.relu)) # Residual block 2 (hidden layers)
model.add(Dense(Nnodes2, activation=tf.nn.relu)) # ...
model.add(Dense(10, activation=tf.nn.relu)) # Output layer of residual block 2/Output of neural net
print('Compiling ...\n')
model.compile(optimizer=Adam(learning_rate=0.001), # Uses Adam algorithm as loss function optimizer
loss='MeanSquaredError', # sets the model to use MSE as loss function during training
metrics=['MeanRelativeError'])
print('Fitting Sequential Model with (X_train, Y_train) ...\n')
#tf.print('X_train = ', X_train, 'with shape', tf.shape(X_train), '\n')
#tf.print('Y_train = ', Y_train, 'with shape', tf.shape(Y_train), '\n')
model.fit(x=X_train, y=Y_train, epochs=M)
#callbacks=[InjectInputCallback(X_train,inject_layer)]
if (MAKE_PLOTS):
plt.plot(history.history['MeanSquareError'])
plt.title('Model Training')
plt.ylabel('Mean Squared Error')
plt.xlabel('Epoch')
plt.legend(['X_Train','Y_Train'], loc='upper right')
plt.show()
#print('Evaluating Sequential Model with Analytic Solution...')
#model.evaluate()
#print('Predicting')
#model.predict()
model.build(tf.shape(X_train))
tf.print(model.summary())
if (__name__ == "__main__"):
print("\n")
MAKE_PLOTS = True # Turns on plots during training and evaluation of neural net
M = int(sys.argv[1]) # Number of training iterations
Ninputs = 3 # Number of node inputs
Nnodes1 = 10 # Number of nodes in layers
Nnodes2 = 30 # Number of nodes in 2nd and 4th layers
inject_layer = 3 # layer of neural net where we inject input data to output of layer
X_train, Y_train, X_test, Y_test = get_dataset()
high_level_neural_net(X_train, Y_train, X_test, Y_test, M, Nnodes1, Nnodes2, inject_layer, af, MAKE_PLOTS)
CodePudding user response：
As stated here, MeanRelativeError metric needs a required argument called normalizer.
Use it in your compile method like this:
metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[.....])])
The normalizer values should be the same shape as predictions