Amazing-Python-Scripts

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import os
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from keras.preprocessing import image
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import matplotlib.pyplot as plt
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import numpy as np
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from keras.utils.np_utils import to_categorical
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import random
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import shutil
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from keras.models import Sequential
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from keras.layers import Dropout, Conv2D, Flatten, Dense, MaxPooling2D, BatchNormalization
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from keras.models import load_model
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def generator(dir, gen=image.ImageDataGenerator(rescale=1./255), shuffle=True, batch_size=1, target_size=(24, 24), class_mode='categorical'):
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    return gen.flow_from_directory(dir, batch_size=batch_size, shuffle=shuffle, color_mode='grayscale', class_mode=class_mode, target_size=target_size)
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BS = 32
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TS = (24, 24)
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train_batch = generator('data/train', shuffle=True,
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                        batch_size=BS, target_size=TS)
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valid_batch = generator('data/valid', shuffle=True,
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                        batch_size=BS, target_size=TS)
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SPE = len(train_batch.classes)//BS
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VS = len(valid_batch.classes)//BS
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print(SPE, VS)
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# img,labels= next(train_batch)
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# print(img.shape)
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model = Sequential([
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    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(24, 24, 1)),
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    MaxPooling2D(pool_size=(1, 1)),
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    Conv2D(32, (3, 3), activation='relu'),
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    MaxPooling2D(pool_size=(1, 1)),
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    # 32 convolution filters used each of size 3x3
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    # again
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    Conv2D(64, (3, 3), activation='relu'),
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    MaxPooling2D(pool_size=(1, 1)),
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    # 64 convolution filters used each of size 3x3
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    # choose the best features via pooling
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    # randomly turn neurons on and off to improve convergence
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    Dropout(0.25),
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    # flatten since too many dimensions, we only want a classification output
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    Flatten(),
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    # fully connected to get all relevant data
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    Dense(128, activation='relu'),
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    # one more dropout for convergence' sake :)
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    Dropout(0.5),
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    # output a softmax to squash the matrix into output probabilities
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    Dense(2, activation='softmax')
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])
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model.compile(optimizer='adam', loss='categorical_crossentropy',
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              metrics=['accuracy'])
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model.fit_generator(train_batch, validation_data=valid_batch,
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                    epochs=15, steps_per_epoch=SPE, validation_steps=VS)
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model.save('models/cnnCat2.h5', overwrite=True)
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