CNN/model.py at master · aberkb/CNN · GitHub

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# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from tensorflow import keras
from keras.layers import Conv2D, MaxPool2D, Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from keras.utils.np_utils import to_categorical

data = pd.read_csv("hmnist_64_64_L.csv")

Y = data["label"]
data.drop(["label"],axis=1, inplace=True)
X = data

img = X.iloc[75].as_matrix()
img = img.reshape(64,64)

X = X.values.reshape(-1,64,64,1)
Y = Y.values
Y = to_categorical(Y)
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state=151)

model = Sequential()

model.add(Conv2D(filters = 128, kernel_size = (5,5),padding = 'same',activation ='relu', input_shape = (64,64,1)))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))

model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'same',activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))

model.add(Conv2D(filters = 64, kernel_size = (3,3),activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))

model.add(Conv2D(filters = 32, kernel_size = (3,3),activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))

model.add(Flatten())

model.add(Dense(256,activation = "relu"))
model.add(Dense(128,activation = "relu"))
model.add(Dense(128,activation = "relu"))
model.add(Dense(32,activation = "relu"))

model.add(Dense(9, activation = "softmax"))

model.compile(optimizer = "Adam" , loss = "categorical_crossentropy", metrics=["accuracy"])

datagen = ImageDataGenerator(
        rotation_range=0.5,
        zoom_range = 0.5,
        width_shift_range=0.5,
        height_shift_range=0.5,
        horizontal_flip=True,
        vertical_flip=True,
)

datagen.fit(x_train)

model.fit_generator(
        datagen.flow(x_train,y_train, batch_size=200),
        epochs = 50,
        validation_data = (x_test,y_test),
        steps_per_epoch=200,
)

Y_pred = model.predict(x_test)
Y_pred_classes = np.argmax(Y_pred,axis = 1)
Y_true_classes = np.argmax(y_test,axis = 1)
print(classification_report(Y_true, Y_pred_classes))

confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
f,ax = plt.subplots(figsize=(18, 16))
sns.heatmap(confusion_mtx, annot=True, linewidths=0.01, cmap="summer_r", fmt='.1f', ax=ax)

plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")

plt.show()