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122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Dropout, Flatten
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from tensorflow.keras.layers import Conv2D, MaxPooling2D
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from tensorflow.keras.layers import BatchNormalization
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from tensorflow.keras.losses import categorical_crossentropy
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.utils import to_categorical
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from tensorflow.keras.callbacks import Callback
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import numpy as np
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import pandas as pd
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import os
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from azureml.core import Run
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# dataset object from the run
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run = Run.get_context()
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dataset = run.input_datasets['prepared_fashion_ds']
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# split dataset into train and test set
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(train_dataset, test_dataset) = dataset.random_split(percentage=0.8, seed=111)
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# load dataset into pandas dataframe
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data_train = train_dataset.to_pandas_dataframe()
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data_test = test_dataset.to_pandas_dataframe()
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img_rows, img_cols = 28, 28
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input_shape = (img_rows, img_cols, 1)
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X = np.array(data_train.iloc[:, 1:])
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y = to_categorical(np.array(data_train.iloc[:, 0]))
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# here we split validation data to optimiza classifier during training
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13)
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# test data
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X_test = np.array(data_test.iloc[:, 1:])
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y_test = to_categorical(np.array(data_test.iloc[:, 0]))
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X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255
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X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255
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X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1).astype('float32') / 255
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batch_size = 256
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num_classes = 10
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epochs = 10
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# construct neuron network
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model = Sequential()
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model.add(Conv2D(32, kernel_size=(3, 3),
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activation='relu',
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kernel_initializer='he_normal',
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input_shape=input_shape))
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model.add(MaxPooling2D((2, 2)))
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model.add(Dropout(0.25))
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model.add(Conv2D(64, (3, 3), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Conv2D(128, (3, 3), activation='relu'))
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model.add(Dropout(0.4))
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model.add(Flatten())
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model.add(Dense(128, activation='relu'))
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model.add(Dropout(0.3))
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model.add(Dense(num_classes, activation='softmax'))
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model.compile(loss=categorical_crossentropy,
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optimizer=Adam(),
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metrics=['accuracy'])
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# start an Azure ML run
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run = Run.get_context()
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class LogRunMetrics(Callback):
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# callback at the end of every epoch
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def on_epoch_end(self, epoch, log):
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# log a value repeated which creates a list
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run.log('Loss', log['loss'])
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run.log('Accuracy', log['accuracy'])
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history = model.fit(X_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=1,
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validation_data=(X_val, y_val),
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callbacks=[LogRunMetrics()])
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score = model.evaluate(X_test, y_test, verbose=0)
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# log a single value
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run.log("Final test loss", score[0])
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print('Test loss:', score[0])
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run.log('Final test accuracy', score[1])
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print('Test accuracy:', score[1])
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plt.figure(figsize=(6, 3))
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plt.title('Fashion MNIST with Keras ({} epochs)'.format(epochs), fontsize=14)
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plt.plot(history.history['accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5)
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plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5)
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plt.legend(fontsize=12)
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plt.grid(True)
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# log an image
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run.log_image('Loss v.s. Accuracy', plot=plt)
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# create a ./outputs/model folder in the compute target
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# files saved in the "./outputs" folder are automatically uploaded into run history
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os.makedirs('./outputs/model', exist_ok=True)
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# serialize NN architecture to JSON
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model_json = model.to_json()
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# save model JSON
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with open('./outputs/model/model.json', 'w') as f:
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f.write(model_json)
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# save model weights
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model.save_weights('./outputs/model/model.h5')
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print("model saved in ./outputs/model folder")
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