Keras 错误日志和标签必须具有相同的形状 ((None, 17, 17, 1) vs (None, 1))

Question

I coded a horse and human classifier as a beginner

# dependencies
import os
import zipfile
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import RMSprop
import matplotlib.pyplot as plt

# Extracting ZipFiles
zip_path1 = r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/files/horse-or-human.zip'
zip_ref1 = zipfile.ZipFile(zip_path1 , 'r')
zip_ref1.extractall(r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/horse-or-human')
zip_ref1.close()

zip_path2 = r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/files/validation-horse-or-human.zip'
zip_ref2 = zipfile.ZipFile(zip_path2 , 'r')
zip_ref2.extractall(r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/validation-horse-or-human')
zip_ref2.close()


# setting up local dir
train_base_dir = r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/horse-or-human'
valid_base_dir = r'C:/Users/91736/Documents/DEEP LEARNING PRACTICE/Week 1/validation-horse-or-human'

# setting up train and test dir
train_horse_dir = os.path.join(train_base_dir , 'horses')
train_human_dir = os.path.join(train_base_dir , 'humans')


valid_horse_dir = os.path.join(valid_base_dir ,'horses')
valid_human_dir = os.path.join(valid_base_dir , 'humans')


# defining model

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(filters = 32 ,
                           kernel_size= (3,3) ,
                           input_shape = (150, 150,3),
                           activation = 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64 , (3,3) , activation = 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128 , (3,3) , activation = 'relu'),
    tf.keras.layers.MaxPool2D(2,2),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512 , activation ='relu'),
    tf.keras.layers.Dense(1 , activation = 'sigmoid')    
    ])

model.compile(loss = 'binary_crossentropy' , optimizer=RMSprop(lr = 0.001) , metrics = ['accuracy'])
model.summary()

# defining augmentation
train_datgen = ImageDataGenerator(rescale = 1./255 ,
                                  rotation_range=40,
                                  width_shift_range= 0.2,
                                  height_shift_range= 0.2,
                                  shear_range= 0.2,
                                  zoom_range = 0.2,
                                  horizontal_flip = True,
                                  fill_mode= 'nearest')

valid_datagen = ImageDataGenerator(rescale = 1./255)




# calling geenrators
train_gen = train_datgen.flow_from_directory(train_base_dir,
                                       target_size = (150, 150),
                                       batch_size = 20,
                                       class_mode = 'binary')

valid_gen = valid_datagen.flow_from_directory(valid_base_dir,
                                        target_size = (150, 150),
                                        batch_size = 20,
                                        class_mode = 'binary')
                                        

history = model.fit_generator(train_gen,
                    validation_data= valid_gen,
                    epochs = 100 ,
                    steps_per_epoch= 10, 
                    validation_steps = 10,
                    verbose = 1)

but when executed run with these warnings

2020-10-26 15:36:58.620164: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled

Figures now render in the Plots pane by default. To make them also appear inline in the Console, uncheck "Mute Inline Plotting" under the Plots pane options menu.

Well as one can see there is no accuracy , loss , validation_loss or validation_accuracy values and the above msg was logged for all 100 epochs, Why is it so

Answer 1

I think you forgot to flatten your output tensor of the layer tf.keras.layers.MaxPool2D(2,2) . Just add a Flatten layer and hopefully it will work.