Comparing layers: a practical case on Mnist¶
In this tutorial, we compare the performance of different layers for mnist dataset
import tensorflow
import numpy as np
import matplotlib.pyplot as plt
plt.rc('font', family='serif')
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
#use_samples=256
use_samples=1024
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = tensorflow.keras.datasets.mnist.load_data()
x_train=x_train[0:use_samples]
y_train=y_train[0:use_samples]
# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")
# convert class vectors to binary class matrices
y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)
x_train shape: (1024, 28, 28, 1)
1024 train samples
10000 test samples
from morpholayers import *
from morpholayers.layers import *
from morpholayers.constraints import *
from morpholayers.regularizers import *
from tensorflow.keras.layers import Input,Conv2D,MaxPooling2D,Flatten,Dropout,Dense
from tensorflow.keras.models import Model
batch_size = 64 #128
epochs = 50
nfilterstolearn=8
filter_size=5
regularizer_parameter=.002
from sklearn.metrics import classification_report,confusion_matrix
def get_model(layer0):
xin=Input(shape=input_shape)
xlayer=layer0(xin)
x=MaxPooling2D(pool_size=(2, 2))(xlayer)
x=Conv2D(32, kernel_size=(3, 3), activation="relu")(x)
x=MaxPooling2D(pool_size=(2, 2))(x)
x=Flatten()(x)
x=Dropout(0.5)(x)
xoutput=Dense(num_classes, activation="softmax")(x)
return Model(xin,outputs=xoutput), Model(xin,outputs=xlayer)
def plot_history(history):
plt.figure()
plt.plot(history.history['loss'],label='loss')
plt.plot(history.history['val_loss'],label='val_loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.grid('on')
plt.legend()
plt.show()
plt.plot(history.history['accuracy'],label='acc')
plt.plot(history.history['val_accuracy'],label='val_acc')
plt.grid('on')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
def plot_output_filters(model):
fig=plt.figure()
Z=model.predict(x_train[0:1,:,:,:])
for i in range(Z.shape[3]):
plt.subplot(2,Z.shape[3]/2,i+1)
plt.imshow(Z[0,:,:,i],cmap='gray',vmax=Z.max(),vmin=Z.min())
#plt.colorbar()
fig.suptitle('Output of Learned Filters for an example')
def plot_filters(model):
Z=model.layers[-1].get_weights()[0]
fig=plt.figure()
for i in range(Z.shape[3]):
plt.subplot(2,Z.shape[3]/2,i+1)
plt.imshow(Z[:,:,0,i],cmap='RdBu',vmax=Z.max(),vmin=Z.min())
fig.suptitle('Learned Filters')
def see_results_layer(layer,lr=.001):
modeli,modellayer=get_model(layer)
modeli.summary()
modeli.compile(loss="categorical_crossentropy", optimizer=tensorflow.keras.optimizers.Adam(lr=lr), metrics=["accuracy"])
historyi=modeli.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test,y_test),
callbacks=[tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5,restore_best_weights=True),
tf.keras.callbacks.ReduceLROnPlateau(patience=2,factor=.5)],verbose=0)
Y_test = np.argmax(y_test, axis=1) # Convert one-hot to index
y_pred = np.argmax(modeli.predict(x_test),axis=1)
CM=confusion_matrix(Y_test, y_pred)
print(CM)
plt.imshow(CM,cmap='hot',vmin=0,vmax=1000)
plt.title('Confusion Matrix')
plt.show()
print(classification_report(Y_test, y_pred))
plot_history(historyi)
plot_filters(modellayer)
plot_output_filters(modellayer)
return historyi
Example of Classical Convolutional Layer¶
histConv=see_results_layer(Conv2D(nfilterstolearn, kernel_size=(filter_size, filter_size),
kernel_regularizer=tensorflow.keras.regularizers.l1_l2(l1=regularizer_parameter,l2=regularizer_parameter), activation="relu"),lr=.01)
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 24, 24, 8) 208
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 12, 12, 8) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 10, 10, 32) 2336
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 800) 0
_________________________________________________________________
dropout (Dropout) (None, 800) 0
_________________________________________________________________
dense (Dense) (None, 10) 8010
=================================================================
Total params: 10,554
Trainable params: 10,554
Non-trainable params: 0
_________________________________________________________________
[[ 957 0 2 0 0 1 12 1 3 4]
[ 0 1116 3 1 0 2 1 5 7 0]
[ 1 2 998 6 3 0 2 13 6 1]
[ 0 0 13 959 0 22 0 7 4 5]
[ 0 0 4 0 941 0 4 3 3 27]
[ 5 1 1 7 0 868 4 1 2 3]
[ 6 4 2 0 1 5 937 0 3 0]
[ 0 4 31 6 2 1 0 969 3 12]
[ 21 0 5 5 10 6 4 7 869 47]
[ 2 5 1 2 6 6 1 6 1 979]]
precision recall f1-score support
0 0.96 0.98 0.97 980
1 0.99 0.98 0.98 1135
2 0.94 0.97 0.95 1032
3 0.97 0.95 0.96 1010
4 0.98 0.96 0.97 982
5 0.95 0.97 0.96 892
6 0.97 0.98 0.97 958
7 0.96 0.94 0.95 1028
8 0.96 0.89 0.93 974
9 0.91 0.97 0.94 1009
accuracy 0.96 10000
macro avg 0.96 0.96 0.96 10000
weighted avg 0.96 0.96 0.96 10000
Example of Dilation Layer¶
histDil=see_results_layer(Dilation2D(nfilterstolearn, padding='valid',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
dilation2d (Dilation2D) (None, 24, 24, 8) 200
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 12, 12, 8) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 10, 10, 32) 2336
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 800) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 800) 0
_________________________________________________________________
dense_1 (Dense) (None, 10) 8010
=================================================================
Total params: 10,546
Trainable params: 10,546
Non-trainable params: 0
_________________________________________________________________
[[ 942 0 8 1 2 1 11 1 3 11]
[ 1 1103 4 2 2 1 3 4 13 2]
[ 4 3 997 5 6 1 0 11 4 1]
[ 0 1 20 904 1 65 0 4 12 3]
[ 3 0 5 0 899 0 9 3 6 57]
[ 2 4 1 7 3 857 7 0 6 5]
[ 5 6 2 0 8 11 923 0 2 1]
[ 0 6 30 10 6 1 0 932 2 41]
[ 23 3 10 10 15 22 5 6 837 43]
[ 1 5 5 15 18 8 1 11 4 941]]
precision recall f1-score support
0 0.96 0.96 0.96 980
1 0.98 0.97 0.97 1135
2 0.92 0.97 0.94 1032
3 0.95 0.90 0.92 1010
4 0.94 0.92 0.93 982
5 0.89 0.96 0.92 892
6 0.96 0.96 0.96 958
7 0.96 0.91 0.93 1028
8 0.94 0.86 0.90 974
9 0.85 0.93 0.89 1009
accuracy 0.93 10000
macro avg 0.93 0.93 0.93 10000
weighted avg 0.94 0.93 0.93 10000
Example of Erosion Layer¶
histEro=see_results_layer(Erosion2D(nfilterstolearn, padding='valid',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
erosion2d (Erosion2D) (None, 24, 24, 8) 200
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 8) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 10, 10, 32) 2336
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 800) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 800) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 8010
=================================================================
Total params: 10,546
Trainable params: 10,546
Non-trainable params: 0
_________________________________________________________________
[[ 961 0 0 0 0 0 10 2 4 3]
[ 0 1079 3 1 1 0 3 4 43 1]
[ 4 2 1000 5 5 0 1 6 8 1]
[ 5 0 21 921 0 28 0 9 24 2]
[ 1 0 5 0 911 0 15 1 6 43]
[ 3 2 1 9 1 849 5 4 13 5]
[ 3 5 4 1 3 18 919 0 5 0]
[ 0 3 33 15 2 0 0 953 2 20]
[ 6 1 5 8 6 5 8 4 907 24]
[ 5 4 4 4 8 8 2 6 2 966]]
precision recall f1-score support
0 0.97 0.98 0.98 980
1 0.98 0.95 0.97 1135
2 0.93 0.97 0.95 1032
3 0.96 0.91 0.93 1010
4 0.97 0.93 0.95 982
5 0.94 0.95 0.94 892
6 0.95 0.96 0.96 958
7 0.96 0.93 0.94 1028
8 0.89 0.93 0.91 974
9 0.91 0.96 0.93 1009
accuracy 0.95 10000
macro avg 0.95 0.95 0.95 10000
weighted avg 0.95 0.95 0.95 10000
Example of Gradient Layer¶
histGrad=see_results_layer(Gradient2D(nfilterstolearn, padding='valid',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_6"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_4 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
gradient2d (Gradient2D) (None, 24, 24, 8) 200
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 12, 12, 8) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 10, 10, 32) 2336
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 5, 5, 32) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 800) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 800) 0
_________________________________________________________________
dense_3 (Dense) (None, 10) 8010
=================================================================
Total params: 10,546
Trainable params: 10,546
Non-trainable params: 0
_________________________________________________________________
[[ 946 0 10 0 0 1 13 3 4 3]
[ 1 1115 3 3 1 2 1 2 4 3]
[ 3 3 984 9 8 1 3 13 5 3]
[ 0 0 21 943 0 34 0 4 2 6]
[ 4 0 3 0 897 0 8 2 3 65]
[ 2 0 2 12 2 852 6 4 9 3]
[ 11 6 3 0 3 12 918 0 5 0]
[ 0 2 31 11 5 2 0 931 1 45]
[ 24 5 20 25 7 12 3 16 802 60]
[ 4 5 6 10 15 7 0 12 1 949]]
precision recall f1-score support
0 0.95 0.97 0.96 980
1 0.98 0.98 0.98 1135
2 0.91 0.95 0.93 1032
3 0.93 0.93 0.93 1010
4 0.96 0.91 0.93 982
5 0.92 0.96 0.94 892
6 0.96 0.96 0.96 958
7 0.94 0.91 0.92 1028
8 0.96 0.82 0.89 974
9 0.83 0.94 0.88 1009
accuracy 0.93 10000
macro avg 0.94 0.93 0.93 10000
weighted avg 0.94 0.93 0.93 10000
Example of Internal Gradient Layer¶
histInternalGrad=see_results_layer(InternalGradient2D(nfilterstolearn, padding='same',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
internal_gradient2d (Interna (None, 28, 28, 8) 200
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 14, 14, 8) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 12, 12, 32) 2336
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 6, 6, 32) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 1152) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 1152) 0
_________________________________________________________________
dense_4 (Dense) (None, 10) 11530
=================================================================
Total params: 14,066
Trainable params: 14,066
Non-trainable params: 0
_________________________________________________________________
[[ 958 0 1 0 0 1 11 5 3 1]
[ 0 1109 7 1 2 1 3 1 11 0]
[ 6 2 983 10 9 0 3 14 4 1]
[ 0 0 14 939 0 35 0 10 5 7]
[ 0 1 2 0 932 0 10 2 4 31]
[ 2 2 1 9 1 861 9 2 3 2]
[ 7 6 4 0 1 14 924 0 2 0]
[ 0 3 19 9 6 1 0 970 4 16]
[ 18 2 24 12 8 8 13 7 850 32]
[ 2 4 4 10 4 6 1 15 5 958]]
precision recall f1-score support
0 0.96 0.98 0.97 980
1 0.98 0.98 0.98 1135
2 0.93 0.95 0.94 1032
3 0.95 0.93 0.94 1010
4 0.97 0.95 0.96 982
5 0.93 0.97 0.95 892
6 0.95 0.96 0.96 958
7 0.95 0.94 0.94 1028
8 0.95 0.87 0.91 974
9 0.91 0.95 0.93 1009
accuracy 0.95 10000
macro avg 0.95 0.95 0.95 10000
weighted avg 0.95 0.95 0.95 10000
Example of Opening Layer¶
histOpening=see_results_layer(Opening2D(nfilterstolearn,padding='valid',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_10"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_6 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
opening2d (Opening2D) (None, 20, 20, 8) 200
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 10, 10, 8) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 8, 8, 32) 2336
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 4, 4, 32) 0
_________________________________________________________________
flatten_5 (Flatten) (None, 512) 0
_________________________________________________________________
dropout_5 (Dropout) (None, 512) 0
_________________________________________________________________
dense_5 (Dense) (None, 10) 5130
=================================================================
Total params: 7,666
Trainable params: 7,666
Non-trainable params: 0
_________________________________________________________________
[[ 949 0 1 5 1 1 9 5 3 6]
[ 0 1077 8 0 1 1 2 4 41 1]
[ 6 5 985 6 4 0 5 8 13 0]
[ 3 0 25 944 0 16 1 6 14 1]
[ 1 0 2 0 893 0 19 1 3 63]
[ 8 6 1 17 1 814 11 4 18 12]
[ 5 8 4 0 5 9 921 0 6 0]
[ 0 5 27 8 2 1 0 972 2 11]
[ 10 3 4 4 6 4 12 1 909 21]
[ 4 4 3 10 9 13 1 8 1 956]]
precision recall f1-score support
0 0.96 0.97 0.97 980
1 0.97 0.95 0.96 1135
2 0.93 0.95 0.94 1032
3 0.95 0.93 0.94 1010
4 0.97 0.91 0.94 982
5 0.95 0.91 0.93 892
6 0.94 0.96 0.95 958
7 0.96 0.95 0.95 1028
8 0.90 0.93 0.92 974
9 0.89 0.95 0.92 1009
accuracy 0.94 10000
macro avg 0.94 0.94 0.94 10000
weighted avg 0.94 0.94 0.94 10000
Example of Closing Layer¶
histClosing=see_results_layer(Closing2D(nfilterstolearn,padding='valid',kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_12"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_7 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
closing2d (Closing2D) (None, 20, 20, 8) 200
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 10, 10, 8) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 8, 8, 32) 2336
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 4, 4, 32) 0
_________________________________________________________________
flatten_6 (Flatten) (None, 512) 0
_________________________________________________________________
dropout_6 (Dropout) (None, 512) 0
_________________________________________________________________
dense_6 (Dense) (None, 10) 5130
=================================================================
Total params: 7,666
Trainable params: 7,666
Non-trainable params: 0
_________________________________________________________________
[[ 959 0 3 2 1 2 9 1 2 1]
[ 0 1106 5 1 1 1 3 4 12 2]
[ 5 2 996 6 7 0 2 10 3 1]
[ 1 1 22 930 0 30 0 6 13 7]
[ 2 1 3 2 916 0 8 2 2 46]
[ 3 2 1 6 0 862 4 1 11 2]
[ 9 6 3 0 5 12 919 0 3 1]
[ 0 5 34 13 3 3 0 929 4 37]
[ 29 4 8 8 13 12 3 11 845 41]
[ 2 5 4 7 5 13 1 19 3 950]]
precision recall f1-score support
0 0.95 0.98 0.96 980
1 0.98 0.97 0.98 1135
2 0.92 0.97 0.94 1032
3 0.95 0.92 0.94 1010
4 0.96 0.93 0.95 982
5 0.92 0.97 0.94 892
6 0.97 0.96 0.96 958
7 0.95 0.90 0.92 1028
8 0.94 0.87 0.90 974
9 0.87 0.94 0.91 1009
accuracy 0.94 10000
macro avg 0.94 0.94 0.94 10000
weighted avg 0.94 0.94 0.94 10000
Example of Toggle Mapping Layer¶
histToggle=see_results_layer(ToggleMapping2D(nfilterstolearn,kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_14"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_8 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
toggle_mapping2d (ToggleMapp (None, 28, 28, 8) 200
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 14, 14, 8) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 12, 12, 32) 2336
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 6, 6, 32) 0
_________________________________________________________________
flatten_7 (Flatten) (None, 1152) 0
_________________________________________________________________
dropout_7 (Dropout) (None, 1152) 0
_________________________________________________________________
dense_7 (Dense) (None, 10) 11530
=================================================================
Total params: 14,066
Trainable params: 14,066
Non-trainable params: 0
_________________________________________________________________
[[ 962 0 1 0 0 3 6 2 3 3]
[ 0 1107 6 1 1 1 3 3 13 0]
[ 6 2 991 10 6 0 1 8 7 1]
[ 0 0 9 930 1 39 0 10 16 5]
[ 0 2 4 0 935 0 9 1 3 28]
[ 6 0 0 12 1 856 3 1 10 3]
[ 14 6 4 0 7 23 900 0 4 0]
[ 1 3 26 7 4 4 0 962 3 18]
[ 35 4 21 11 15 18 6 10 814 40]
[ 9 4 2 14 17 10 0 25 3 925]]
precision recall f1-score support
0 0.93 0.98 0.96 980
1 0.98 0.98 0.98 1135
2 0.93 0.96 0.95 1032
3 0.94 0.92 0.93 1010
4 0.95 0.95 0.95 982
5 0.90 0.96 0.93 892
6 0.97 0.94 0.95 958
7 0.94 0.94 0.94 1028
8 0.93 0.84 0.88 974
9 0.90 0.92 0.91 1009
accuracy 0.94 10000
macro avg 0.94 0.94 0.94 10000
weighted avg 0.94 0.94 0.94 10000
Example of Top Hat Opening Layer¶
histTopHatOpening=see_results_layer(TopHatOpening2D(nfilterstolearn,kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_9 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
top_hat_opening2d (TopHatOpe (None, 28, 28, 8) 200
_________________________________________________________________
max_pooling2d_16 (MaxPooling (None, 14, 14, 8) 0
_________________________________________________________________
conv2d_9 (Conv2D) (None, 12, 12, 32) 2336
_________________________________________________________________
max_pooling2d_17 (MaxPooling (None, 6, 6, 32) 0
_________________________________________________________________
flatten_8 (Flatten) (None, 1152) 0
_________________________________________________________________
dropout_8 (Dropout) (None, 1152) 0
_________________________________________________________________
dense_8 (Dense) (None, 10) 11530
=================================================================
Total params: 14,066
Trainable params: 14,066
Non-trainable params: 0
_________________________________________________________________
[[ 963 0 1 0 0 1 7 2 3 3]
[ 0 1112 5 2 1 2 1 3 9 0]
[ 6 5 984 12 5 0 2 12 4 2]
[ 1 1 13 938 0 30 0 10 14 3]
[ 0 1 3 0 933 0 9 2 4 30]
[ 2 1 1 5 0 867 5 2 8 1]
[ 10 5 3 0 3 14 921 0 2 0]
[ 1 3 23 11 3 2 0 962 2 21]
[ 27 1 8 6 10 10 8 5 860 39]
[ 10 5 3 10 11 10 0 13 3 944]]
precision recall f1-score support
0 0.94 0.98 0.96 980
1 0.98 0.98 0.98 1135
2 0.94 0.95 0.95 1032
3 0.95 0.93 0.94 1010
4 0.97 0.95 0.96 982
5 0.93 0.97 0.95 892
6 0.97 0.96 0.96 958
7 0.95 0.94 0.94 1028
8 0.95 0.88 0.91 974
9 0.91 0.94 0.92 1009
accuracy 0.95 10000
macro avg 0.95 0.95 0.95 10000
weighted avg 0.95 0.95 0.95 10000
Example of Top Hat Closing Layer¶
histTopHatClosing=see_results_layer(TopHatClosing2D(nfilterstolearn,kernel_size=(filter_size, filter_size),kernel_regularization=L1L2Lattice(l1=regularizer_parameter,l2=regularizer_parameter)),lr=.01)
Model: "model_18"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_10 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
top_hat_closing2d (TopHatClo (None, 28, 28, 8) 200
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 14, 14, 8) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 12, 12, 32) 2336
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 6, 6, 32) 0
_________________________________________________________________
flatten_9 (Flatten) (None, 1152) 0
_________________________________________________________________
dropout_9 (Dropout) (None, 1152) 0
_________________________________________________________________
dense_9 (Dense) (None, 10) 11530
=================================================================
Total params: 14,066
Trainable params: 14,066
Non-trainable params: 0
_________________________________________________________________
[[ 952 0 4 2 1 6 6 3 5 1]
[ 0 1122 3 0 1 0 4 4 1 0]
[ 15 2 944 16 9 2 8 10 25 1]
[ 2 0 18 893 1 48 0 8 32 8]
[ 2 0 5 0 873 0 15 11 8 68]
[ 4 7 4 19 4 809 8 6 24 7]
[ 12 5 4 1 11 28 891 0 5 1]
[ 0 6 29 6 5 0 0 956 3 23]
[ 5 2 17 19 4 11 10 10 861 35]
[ 11 2 5 9 18 6 1 28 7 922]]
precision recall f1-score support
0 0.95 0.97 0.96 980
1 0.98 0.99 0.98 1135
2 0.91 0.91 0.91 1032
3 0.93 0.88 0.90 1010
4 0.94 0.89 0.91 982
5 0.89 0.91 0.90 892
6 0.94 0.93 0.94 958
7 0.92 0.93 0.93 1028
8 0.89 0.88 0.89 974
9 0.86 0.91 0.89 1009
accuracy 0.92 10000
macro avg 0.92 0.92 0.92 10000
weighted avg 0.92 0.92 0.92 10000
Summary of Results¶
Best Results for method in training accurary:
print('Linear Convolution: ',max(histConv.history['accuracy']))
print('Dilation: ',max(histDil.history['accuracy']))
print('Erosion: ',max(histEro.history['accuracy']))
print('Gradient: ',max(histGrad.history['accuracy']))
print('Internal Gradient: ',max(histInternalGrad.history['accuracy']))
print('Toggle: ',max(histToggle.history['accuracy']))
print('TopHatOpening: ',max(histTopHatOpening.history['accuracy']))
print('TopHatClosing: ',max(histTopHatClosing.history['accuracy']))
Linear Convolution: 0.9970703125
Dilation: 0.98046875
Erosion: 0.99609375
Gradient: 0.974609375
Internal Gradient: 0.9931640625
Toggle: 0.99609375
TopHatOpening: 0.99609375
TopHatClosing: 0.998046875
Best Results for method in validation accurary:
print('Linear Convolution: ',max(histConv.history['val_accuracy']))
print('Dilation: ',max(histDil.history['val_accuracy']))
print('Erosin: ',max(histEro.history['val_accuracy']))
print('Gradient: ',max(histGrad.history['val_accuracy']))
print('Internal Gradient: ',max(histInternalGrad.history['val_accuracy']))
print('Toggle: ',max(histToggle.history['val_accuracy']))
print('TopHatOpening: ',max(histTopHatOpening.history['val_accuracy']))
print('TopHatClosing: ',max(histTopHatClosing.history['val_accuracy']))
Linear Convolution: 0.9595000147819519
Dilation: 0.9361000061035156
Erosin: 0.9491999745368958
Gradient: 0.9355999827384949
Internal Gradient: 0.9495000243186951
Toggle: 0.9388999938964844
TopHatOpening: 0.949400007724762
TopHatClosing: 0.9240000247955322
Comparison of Validaciones accuracy
plt.figure(figsize=(20,20))
plt.plot(histConv.history['val_accuracy'],label='Linear Convolution')
plt.plot(histDil.history['val_accuracy'],label='Dilation')
plt.plot(histEro.history['val_accuracy'],label='Erosion')
plt.plot(histGrad.history['val_accuracy'],label='Gradient')
plt.plot(histInternalGrad.history['val_accuracy'],label='Internal Gradient')
plt.plot(histToggle.history['val_accuracy'],label='Toggle')
plt.plot(histTopHatOpening.history['val_accuracy'],label='TopHatOpening')
plt.plot(histTopHatClosing.history['val_accuracy'],label='TopHatClosing')
plt.xlabel('Epocs')
plt.ylabel('Validation Accuracy')
plt.grid()
plt.legend()
<matplotlib.legend.Legend at 0x1a436065c0>
