ValueError：形状 (None, 9) 和 (None, 10) 不兼容

Question

I have a dataset with 565 features and 10 different columns on the prediction site for predicting labels in the training model.Here is the model summary dimensions:

_________________________________________________________________
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv1d (Conv1D)              (None, 564, 64)           256
_________________________________________________________________
flatten (Flatten)            (None, 36096)             0
_________________________________________________________________
dense (Dense)                (None, 50)                1804850
_________________________________________________________________
dense_1 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_2 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_3 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_4 (Dense)              (None, 10)                510
=================================================================
Total params: 1,813,266
Trainable params: 1,813,266
Non-trainable params: 0
_________________________________________________________________

Here is the code I have used:

import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, Flatten
from tensorflow.keras import optimizers
from sklearn.metrics import confusion_matrix
import tensorflow as tf
import tensorflow.keras.metrics

    data = pd.read_csv('Step1_reducedfile.csv',skiprows = 1,header = None)
    data = data.sample(frac=1).reset_index(drop=True)
    train_X = data[0:data.shape[0],0:566]
    train_y = data[0:data.shape[0],566:data.shape[1]]
    train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
    
    import random
    
    neurons = 50
    
            
    strategy = tensorflow.distribute.MirroredStrategy()             
    with strategy.scope():
        model = tf.keras.Sequential([
          tf.keras.layers.Conv1D(64,kernel_size = 3,activation='relu',input_shape=train_X.shape[1:]),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(10, activation='softmax'),])
        model.summary()
        sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.24, nesterov=True)
        model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy',tensorflow.keras.metrics.Precision()])
        model.summary()
        results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
        print(results)

I am getting the following error:

ValueError: in user code:

/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_strategy.py:585 _call_for_each_replica
    self._container_strategy(), fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:96 call_for_each_replica
    return _call_for_each_replica(strategy, fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:237 _call_for_each_replica
    coord.join(threads)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:389 join
    six.reraise(*self._exc_info_to_raise)
/usr/local/lib/python3.6/site-packages/six.py:703 reraise
    raise value
/usr/local/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception
    yield
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:323 run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
    outputs = model.train_step(data)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:749 train_step
    y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
    loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:149 __call__
    losses = ag_call(y_true, y_pred)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:253 call  **
    return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:1535 categorical_crossentropy
    return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/backend.py:4687 categorical_crossentropy
    target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
    raise ValueError("Shapes %s and %s are incompatible" % (self, other))

ValueError: Shapes (None, 9) and (None, 10) are incompatible

Answer 1

That error shows that you are giving a wrong shape of label array to your model. It is s expecting an array of shape (None, 9), while you are giving an array of shape (None, 10). This may be because your dataset has 9 classes as rightly mentioned by Dr.Snoopy.

For the benefit of community here i am providing complete working code.

import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, Flatten
from tensorflow.keras import optimizers
from sklearn.metrics import confusion_matrix
import tensorflow as tf
import tensorflow.keras.metrics

    data = pd.read_csv('Step1_reducedfile.csv',skiprows = 1,header = None)
    data = data.sample(frac=1).reset_index(drop=True)
    train_X = data[0:data.shape[0],0:566]
    train_y = data[0:data.shape[0],566:data.shape[1]]
    train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
    
    import random
    
    neurons = 50
    
            
    strategy = tensorflow.distribute.MirroredStrategy()             
    with strategy.scope():
        model = tf.keras.Sequential([
          tf.keras.layers.Conv1D(64,kernel_size = 3,activation='relu',input_shape=train_X.shape[1:]),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(9, activation='softmax'),])
        model.summary()
        sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.24, nesterov=True)
        model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy',tensorflow.keras.metrics.Precision()])
        model.summary()
        results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
        print(results)