使用 Tensorflow/Keras 运行图像分类 model 时出现 ValueError

Question

me and my partner are making a model that classifies a given image as wearing a mass correctly or incorrectly. When we try to run our model, a ValueError pops up. We're both beginners in learning Keras and Tensorflow, so please cut a bit of slack on us. We're using Jupyter notebook to run our model. Please tell us if you need more info.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, Dropout, MaxPooling2D
import pickle

X = pickle.load(open('X.pickle','rb'))
y = pickle.load(open('y.pickle', 'rb'))

model = Sequential()

model.add(Conv2D(128, (6, 6), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128, (4, 4)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
             optimizer='adam',
             metrics=['accuracy'])

model.fit(X, y, batch_size=32, epochs=4, validation_split=0.3)

Here's the output:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-f184ee158893> in <module>
     29              metrics=['accuracy'])
     30 
---> 31 model.fit(X, y, batch_size=32, epochs=4, validation_split=0.3)

/Users/user/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
   1534         steps_name='steps_per_epoch',
   1535         steps=steps_per_epoch,
-> 1536         validation_split=validation_split)
   1537 
   1538     # Prepare validation data.

/Users/user/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split)
    990         x, y, sample_weight = next_element
    991     x, y, sample_weights = self._standardize_weights(x, y, sample_weight,
--> 992                                                      class_weight, batch_size)
    993     return x, y, sample_weights
    994 

/Users/user/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _standardize_weights(self, x, y, sample_weight, class_weight, batch_size)
   1152           feed_output_shapes,
   1153           check_batch_axis=False,  # Don't enforce the batch size.
-> 1154           exception_prefix='target')
   1155 
   1156       # Generate sample-wise weight values given the `sample_weight` and

/Users/user/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    321                            ': expected ' + names[i] + ' to have ' +
    322                            str(len(shape)) + ' dimensions, but got array '
--> 323                            'with shape ' + str(data_shape))
    324         if not check_batch_axis:
    325           data_shape = data_shape[1:]

ValueError: Error when checking target: expected activation_11 to have 2 dimensions, but got array with shape (11504, 100, 100, 3)

Answer 1

if your example got 2 classes just add a dense layer with one unit between your 2 last layers

model.add(Flatten())

#Add this
model.add(Dense(1))

model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
             optimizer='adam',
             metrics=['accuracy'])