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'))
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 machine learning. I followed a tutorial to classify if the user is similing or not. I created this code:
def get_model(input_size, classes=7):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape =input_size))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(2, 2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(0.01)))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
#Compliling the model
model.compile(optimizer=Adam(lr=0.0001, decay=1e-6),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
if i try to predict an array from flow_from_directory its working fine but i would like to predict it using the following code:
final_image = cv2.imread('./tesimg.jpeg')
final_image = np.expand_dims(final_image, axis=0)
final_image = final_image/255.0
The problem is that i'm getting this error:
UnimplementedError: Graph execution error:
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.
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 am trying to train a model based on CNN,
Training data is the image of lunar lander, but the performance of model is not good, the accuracy is about 45%, I try to add more layer to improve it, but it still doesn't work well, could any one provide some ideas about how to improve it.
Label: up down left right (0,1,2,3)
Sample rate: 0.1
The Training data has converted from image to data
Label:
here is some of my code:
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(CHANNELS, ROWS, COLS), activation='relu'))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))
.summary()
I would experiment a lower dropout rate. Dropouts are used to prevent overfitting. It looks like your model isn't even fitting in the first place.