Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Zadanie4_Semenov_II_DRPK47.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/11fNvzrVniDSjEVdE-ZnejpPvvA88ApfY
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow.keras as keras
+import tensorflow.keras.datasets
+from tensorflow.keras.datasets import fashion_mnist
+from tensorflow.keras.layers import Input, Dense
+
+(train_x, train_y), (test_x, test_y) = fashion_mnist.load_data()
+
+train_x = train_x / 255
+test_x = test_x / 255
+
+train_x = np.reshape(train_x, (len(train_x), 28 * 28))
+test_x = np.reshape(test_x, (len(test_x), 28 * 28))
+
+inputs = Input(shape = (28*28, ))
+x = Dense(150, activation = 'relu')(inputs)
+x = Dense(400, activation = 'relu')(x)
+x = Dense(10, activation = 'relu')(x)
+encoder = Dense(3, activation = 'linear')(x)
+
+
+inputs_dec = Input(shape = (3, ))
+x = Dense(10, activation = 'relu')(inputs_dec)
+x = Dense(40, activation = 'relu')(x)
+x = Dense(150, activation = 'relu')(x)
+decoder = Dense(28*28, activation = 'relu')(x)
+
+encoder_model = keras.Model(inputs, encoder)
+decoder_model = keras.Model(inputs_dec, decoder)
+autoenc = keras.Model(inputs, decoder_model(encoder_model(inputs)))
+
+autoenc.compile(optimizer='adam', loss='mean_squared_error', metrics = ['accuracy'])
+
+autoenc.fit(train_x, train_x, epochs = 20, batch_size=50)
+
+y = autoenc.predict(test_x[:12])
+plt.imshow(y[5].reshape(28, 28), cmap = 'gray')