model = Sequential()
model.add(Conv2D(128, (3, 3), activation='relu', input_shape=(64, 64, 3), padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation = 'softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=[tf.keras.metrics.Recall()])
This code works fine for metrics=['accuracy']), but it shows ValueError: Shapes (None, 1) and (None, 5) are incompatible for metrics=[tf.keras.metrics.Recall()])
Please help me. Thanks in advance.
Recall makes sense only for binary classification. Your final layer has 5 nodes, which essentially means you have 5 classes. You should change recall to another metric. Documentation should help you choose an appropriate metric for your model. Categorical accuracy should be good enough to get started.
Related
I am running a 3 class classification problem with around 7200 total images using CNN. I have used 80:20 split and the accuracy and loss curves are attached along with the code.
Can someone explain why the validation accuracy is higher then the training and similarly training loss is higher than the validation loss. Although the difference is marginal (around 1%), I am not sure if its acceptable or I need to improve the model.
Here is the model I am running with batch_size = 64
# define cnn model
def define_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(64, 64, 3)))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
# compile model
opt = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
I tried various other models but the accuracy and loss curves didn't seem good. This one seems good but I was expecting the training accuracy to be higher than the validation accuracy
I'm pretty new to maching learning and when I was looking at a tutorial for a convolutional neural network I wanted to experiment on my own on how to increase accuracy. However, when I tried to add another convolutional and pooling layer to my model it displayed an error message. This is before I added the layer:
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 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.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(62))
And this is after:
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 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.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(62))
This is the error message it gave me:
ValueError: Negative dimension size caused by subtracting 3 from 1 for '{{node conv2d_36/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](max_pooling2d_26/MaxPool, conv2d_36/Conv2D/ReadVariableOp)' with input shapes: [?,1,1,64], [3,3,64,64]. site:stackoverflow.com
This is because you reduce the dimensionality too much inside your network. use padding='same' in your convolutional layer to avoid this dimensionality error
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', padding='same',
input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(62))
model.summary()
I'm using Keras to implement CNN. People often use Conv2D to do classification tasks. However, I want to get relationships between two images, then I decide to try Conv3D. However, I couldn't manage the dimension output from Conv3D and match the following layers.
More specifically, I want to apply (5,5,2) filter on two stacked images which are (480, 640, 2), and output(480, 640, 1) tensor.
Original Conv2D code: (work fine)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',input_shape=(480, 640, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
...
Conv3D code: (Don't know how to concatenate Conv3D and MaxPooling2D)
model.add(Conv3D(32, 2, input_shape=(480, 640, 2, 1), data_format="channels_last"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
...
Stack both the images (remember to stack acc. to the backend you are using, theano is channels_first and tensorflow is channels_last) and pass 2 as the number of channels in Conv2D.
Or if you have many channels for each images, then again stack them up and pass the total number of channels to Conv2D.
I'm using the below keras model to train a neural network to tell 52 game cards 23456789TJQA each with Club, Diamond, Heart and Spade apart.
The model is working quite well but occasionally has problems telling Club and Diamond apart, as they are the most similar (and the difference is quite granular). I was wondering if anybody has some suggestions in what way I can improve the below model?
I've tried different things, like converting everything to black and white, grayscale, smoothing, augmentation etc, but nothing seems to solve that problem.
The pictures are all 15x50 pixels, with 1 channel, so the input shape is (15,50,1)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape, activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
I want to build an end-to-end trainable model with the following proprieties:
CNN to extract features from image
The features is reshaped to a matrix
Each row of this matrix is then fed to LSTM1
Each column of this matrix is then fed to LSTM2
The output of LSTM1 and LSTM2 are concatenated for the final output
(it's more or less similar to Figure 2 in this paper: https://arxiv.org/pdf/1611.07890.pdf)
My problem now is after the reshape, how can I feed the values of feature matrix to LSTM with Keras or Tensorflow?
This is my code so far with VGG16 net (also a link to Keras issues):
# VGG16
model = Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(224, 224, 3)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
# block 2
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
# block 3
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
# block 4
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
# block 5
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
# block 6
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dense(4096, activation='relu'))
# reshape the feature 4096 = 64 * 64
model.add(Reshape((64, 64)))
# How to feed each row of this to LSTM?
# This is my first solution but it doesn’t look correct:
# model.add(LSTM(256, input_shape=(64, 1))) # 256 hidden units, sequence length = 64, feature dim = 1
Consider building your CNN model with Conv2D and MaxPool2D layers, until you reach your Flatten layer, because the vectorized output from the Flatten layer will be you input data to the LSTM part of your structure.
So, build your CNN model like this:
model_cnn = Sequential()
model_cnn.add(Conv2D...)
model_cnn.add(MaxPooling2D...)
...
model_cnn.add(Flatten())
Now, this is an interesting point, the current version of Keras has some incompatibility with some TensorFlow structures that will not let you stack your entire layers in just one Sequential object.
So it's time to use the Keras Model Object to complete you neural network with a trick:
input_lay = Input(shape=(None, ?, ?, ?)) #dimensions of your data
time_distribute = TimeDistributed(Lambda(lambda x: model_cnn(x)))(input_lay) # keras.layers.Lambda is essential to make our trick work :)
lstm_lay = LSTM(?)(time_distribute)
output_lay = Dense(?, activation='?')(lstm_lay)
And finally, now it's time to put together our 2 separated models:
model = Model(inputs=[input_lay], outputs=[output_lay])
model.compile(...)
OBS: Note that you can substitute my model_cnn example by your VGG without including the top layers, once the vectorized output from the VGG Flatten layer will be the input of the LSTM model.