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history = model.fit_generator(train_generator,
                              validation_data=val_generator,
                              epochs=50,
                              callbacks=[model_ckpt,reduce_lr,early_stop],
                              verbose=1)

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model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

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model_ckpt = ModelCheckpoint('BrailleNet.h5',save_best_only=True)
reduce_lr = ReduceLROnPlateau(patience=8,verbose=0)
early_stop = EarlyStopping(patience=15,verbose=1)

entry = layers.Input(shape=(28,28,3))
x = layers.SeparableConv2D(64,(3,3),activation='relu')(entry)
x = layers.MaxPooling2D((2,2))(x)
x = layers.SeparableConv2D(128,(3,3),activation='relu')(x)
x = layers.MaxPooling2D((2,2))(x)
x = layers.SeparableConv2D(256,(2,2),activation='relu')(x)

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os.mkdir('./images/')
alpha = 'a'
for i in range(0, 26): 
    os.mkdir('./images/' + alpha)
    alpha = chr(ord(alpha) + 1)

for file in os.listdir(rootdir):
    letter = file[0]
    try:
        shutil.copy(rootdir+file, './images/' + letter + '/' + file)

shyamal18122000

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from sklearn.preprocessing import LabelEncoder
import PIL
import cv2
import shutil

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from tensorflow.keras.optimizers import Adam

model.compile(
    loss="categorical_crossentropy",
    optimizer=Adam(lr=0.001),
    metrics=['accuracy']
)

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train_model = model.fit(
                  X_train, Y_train,
                  batch_size=128,
                  epochs=100,
                  verbose=1,
                  validation_data=(X_val, Y_val)
              )

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model = Sequential([
    #first convolution
    Conv2D(32, (3,3), padding='same', activation='relu',kernel_initializer='he_normal', input_shape=(28,28, 1)),
    MaxPooling2D(2,2),
    
    #second convolution
    Conv2D(64, (3,3),padding='same', activation='relu'),
    MaxPooling2D(2,2),
    Dropout(0.2),
    

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def preprocessing(raw):
    label = tf.keras.utils.to_categorical(raw.label, 10)
    num_of_images = raw.shape[0]
    x_as_array = raw.values[:,1:]
    x_shaped_array = x_as_array.reshape(num_of_images, 28, 28, 1)
    image = x_shaped_array / 255
    return image, label

X, Y = preprocessing(train_fashion_data)
X_test, Y_test = preprocessing(test_fashion_data)

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train_fashion_data = pd.read_csv("fashion-mnist_train.csv")
test_fashion_data = pd.read_csv("fashion-mnist_test.csv")