Tensorflow: ValueError: Shapes (None, 1) and (None, 2) are incompatible

Question

I am very new to neural network and machine learning however, I have created training data for clouds and no clouds and used the same model used for one of my hand gesture project. When I used this model, I had initially encountered a similar error message, where it said:

ValueError: Shapes (64, 10) and (64, 4) are incompatible

I am using same model in the past for some hand gesture code. I had a similar error message in the past because training data only had 4 option and my model was trying to fit 10 options, from my understanding. And had to change the final number of neurons from 10 to 4.

import numpy as np 
import matplotlib.pyplot as plt 
import os
import cv2
import random
from tqdm import tqdm
import tensorflow as tf 
from keras import layers
from keras import models
#Loading the traning data
DATADIR = "D:/Python_Code/Machine_learning/Cloud_Trainin_Data"
CATEGORIES = ["Clouds", "Normal_Terrain"]
training_data = []
for category in CATEGORIES:  # do dogs and cats
    path = os.path.join(DATADIR,category)  # create path to dogs and cats
    class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat
    for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats       
        img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array
        height = 1000
        dim = None
        (h, w) = img_array.shape[:2]
        r = height / float(h)
        dim = (int(w * r), height)
        resized = cv2.resize(img_array, dim, interpolation = cv2.INTER_AREA)
        training_data.append([resized, class_num])  # add this to our training_data

print(len(training_data))
random.shuffle(training_data)
for sample in training_data[:10]:
    print(sample[1])

X = []
y = []

for features,label in training_data:
    X.append(features)
    y.append(label)

hh,ww = resized.shape
X = np.array(X).reshape(-1, hh, ww, 1)
y = np.array(y)
#Normalizing the data
X = X/255.0

#Building the Model
model=models.Sequential()
model.add(layers.Conv2D(32, (5, 5), strides=(2, 2), activation='relu', input_shape=X.shape[1:])) 
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu')) 
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation='relu'))
model.add(layers.Dense(2, activation='softmax'))
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
EPOCHS = 1
history = model.fit(X, y, batch_size = 5, epochs=EPOCHS, validation_split=0.1)
accuray, loss = model.evaluate(X, y)

print(accuray, loss)
model.save('Clouds.model')
loss = history.history["loss"]
acc = history.history["accuracy"]
epoch = np.arange(EPOCHS)
plt.plot(epoch, loss)
# plt.plot(epoch, val_loss)
plt.plot(epoch, acc, color='red')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')

plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Training Loss')
plt.legend(['train', 'val'])
plt.show()

However in this case, when I apply the same solution, the error message keeps appearing.

Any suggestions?

Answer 1

Looks like you are trying to implement binary classification problem. As suggested @Tfer2 change loss function categorical_crossentropy to binary_crossentropy .

Working sample code

import tensorflow as tf
import numpy as np
from tensorflow.keras import datasets
import tensorflow.keras as keras

train_images, test_images = train_images / 255.0, test_images / 255.0

#input_shape=(X_train.shape[0],X_train.shape[1],X_train.shape[2])
input_shape = (32, 32, 3)

model = tf.keras.Sequential()
#first layer
model.add(keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=input_shape))
model.add(keras.layers.MaxPool2D((3,3),strides=(2,2),padding='same'))

#second layer
model.add(keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=input_shape))
model.add(keras.layers.MaxPool2D((3,3),strides=(2,2),padding='same'))
#third layer
model.add(keras.layers.Conv2D(64,(2,2),activation='relu',input_shape=input_shape))
model.add(keras.layers.MaxPool2D((2,2),strides=(2,2),padding='same'))
#flatten
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128,activation='relu'))
model.add(keras.layers.Dropout(0.3))
#output
model.add(keras.layers.Dense(2,activation='softmax'))

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(train_images,train_labels,validation_data=(test_images,test_labels),batch_size=32,epochs=50)

Output

Epoch 1/50
1563/1563 [==============================] - 25s 10ms/step - loss: 1.8524 - accuracy: 0.3163 - val_loss: 1.5800 - val_accuracy: 0.4311
Epoch 2/50
1563/1563 [==============================] - 15s 9ms/step - loss: 1.5516 - accuracy: 0.4329 - val_loss: 1.4234 - val_accuracy: 0.4886
Epoch 3/50
1563/1563 [==============================] - 17s 11ms/step - loss: 1.4365 - accuracy: 0.4789 - val_loss: 1.3575 - val_accuracy: 0.5111
Epoch 4/50
1563/1563 [==============================] - 14s 9ms/step - loss: 1.3624 - accuracy: 0.5098 - val_loss: 1.2803 - val_accuracy: 0.5471
Epoch 5/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.3069 - accuracy: 0.5322 - val_loss: 1.2305 - val_accuracy: 0.5663
Epoch 6/50
1563/1563 [==============================] - 12s 8ms/step - loss: 1.2687 - accuracy: 0.5471 - val_loss: 1.1839 - val_accuracy: 0.5796
Epoch 7/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.2243 - accuracy: 0.5668 - val_loss: 1.1430 - val_accuracy: 0.5940
Epoch 8/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.1891 - accuracy: 0.5800 - val_loss: 1.1261 - val_accuracy: 0.6061
Epoch 9/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.1568 - accuracy: 0.5916 - val_loss: 1.0998 - val_accuracy: 0.6157
Epoch 10/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.1219 - accuracy: 0.6053 - val_loss: 1.0769 - val_accuracy: 0.6210
Epoch 11/50
1563/1563 [==============================] - 12s 8ms/step - loss: 1.0993 - accuracy: 0.6148 - val_loss: 1.0369 - val_accuracy: 0.6335
Epoch 12/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.0709 - accuracy: 0.6232 - val_loss: 1.0119 - val_accuracy: 0.6463
Epoch 13/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.0473 - accuracy: 0.6302 - val_loss: 0.9964 - val_accuracy: 0.6516
Epoch 14/50
1563/1563 [==============================] - 13s 8ms/step - loss: 1.0252 - accuracy: 0.6419 - val_loss: 0.9782 - val_accuracy: 0.6587
Epoch 15/50
1563/1563 [==============================] - 12s 8ms/step - loss: 1.0035 - accuracy: 0.6469 - val_loss: 0.9569 - val_accuracy: 0.6644
Epoch 16/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.9836 - accuracy: 0.6572 - val_loss: 0.9586 - val_accuracy: 0.6633
Epoch 17/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.9656 - accuracy: 0.6614 - val_loss: 0.9192 - val_accuracy: 0.6790
Epoch 18/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.9506 - accuracy: 0.6679 - val_loss: 0.9133 - val_accuracy: 0.6781
Epoch 19/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.9273 - accuracy: 0.6756 - val_loss: 0.9046 - val_accuracy: 0.6824
Epoch 20/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.9129 - accuracy: 0.6795 - val_loss: 0.8855 - val_accuracy: 0.6910
Epoch 21/50
1563/1563 [==============================] - 14s 9ms/step - loss: 0.8924 - accuracy: 0.6873 - val_loss: 0.8886 - val_accuracy: 0.6927
Epoch 22/50
1563/1563 [==============================] - 16s 10ms/step - loss: 0.8840 - accuracy: 0.6905 - val_loss: 0.8625 - val_accuracy: 0.7013
Epoch 23/50
1563/1563 [==============================] - 15s 9ms/step - loss: 0.8655 - accuracy: 0.6980 - val_loss: 0.8738 - val_accuracy: 0.6950
Epoch 24/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.8543 - accuracy: 0.7019 - val_loss: 0.8454 - val_accuracy: 0.7064
Epoch 25/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.8388 - accuracy: 0.7056 - val_loss: 0.8354 - val_accuracy: 0.7063
Epoch 26/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.8321 - accuracy: 0.7115 - val_loss: 0.8244 - val_accuracy: 0.7161
Epoch 27/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.8169 - accuracy: 0.7163 - val_loss: 0.8390 - val_accuracy: 0.7084
Epoch 28/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.8071 - accuracy: 0.7190 - val_loss: 0.8372 - val_accuracy: 0.7127
Epoch 29/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.7949 - accuracy: 0.7219 - val_loss: 0.7990 - val_accuracy: 0.7217
Epoch 30/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7861 - accuracy: 0.7273 - val_loss: 0.7940 - val_accuracy: 0.7281
Epoch 31/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.7750 - accuracy: 0.7299 - val_loss: 0.7933 - val_accuracy: 0.7262
Epoch 32/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7635 - accuracy: 0.7373 - val_loss: 0.7964 - val_accuracy: 0.7254
Epoch 33/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7537 - accuracy: 0.7361 - val_loss: 0.7891 - val_accuracy: 0.7259
Epoch 34/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.7460 - accuracy: 0.7410 - val_loss: 0.7893 - val_accuracy: 0.7257
Epoch 35/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7366 - accuracy: 0.7448 - val_loss: 0.7713 - val_accuracy: 0.7332
Epoch 36/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7275 - accuracy: 0.7492 - val_loss: 0.8443 - val_accuracy: 0.7095
Epoch 37/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.7257 - accuracy: 0.7478 - val_loss: 0.7583 - val_accuracy: 0.7365
Epoch 38/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.7097 - accuracy: 0.7535 - val_loss: 0.7497 - val_accuracy: 0.7458
Epoch 39/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.7091 - accuracy: 0.7554 - val_loss: 0.7588 - val_accuracy: 0.7370
Epoch 40/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6945 - accuracy: 0.7576 - val_loss: 0.7583 - val_accuracy: 0.7411
Epoch 41/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6888 - accuracy: 0.7592 - val_loss: 0.7481 - val_accuracy: 0.7408
Epoch 42/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.6829 - accuracy: 0.7634 - val_loss: 0.7372 - val_accuracy: 0.7456
Epoch 43/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6742 - accuracy: 0.7665 - val_loss: 0.7324 - val_accuracy: 0.7475
Epoch 44/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.6646 - accuracy: 0.7679 - val_loss: 0.7444 - val_accuracy: 0.7425
Epoch 45/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.6613 - accuracy: 0.7686 - val_loss: 0.7294 - val_accuracy: 0.7506
Epoch 46/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6499 - accuracy: 0.7712 - val_loss: 0.7335 - val_accuracy: 0.7470
Epoch 47/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.6446 - accuracy: 0.7759 - val_loss: 0.7223 - val_accuracy: 0.7544
Epoch 48/50
1563/1563 [==============================] - 12s 8ms/step - loss: 0.6376 - accuracy: 0.7793 - val_loss: 0.7259 - val_accuracy: 0.7496
Epoch 49/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6341 - accuracy: 0.7803 - val_loss: 0.7705 - val_accuracy: 0.7355
Epoch 50/50
1563/1563 [==============================] - 13s 8ms/step - loss: 0.6234 - accuracy: 0.7820 - val_loss: 0.7116 - val_accuracy: 0.7562