import tensorflow as tf import numpy as np import keras data = keras.datasets.fashion_mnist (training_images, training_labels), (test_images, test_labels) = data.load_data() # Normalizing images training_images = training_images / 255.0 test_images = test_images / 255.0 # Defining the model model = keras.Sequential([ keras.layers.Flatten(input_shape=(28,28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) # Compiling the model model.compile( optimizer='adam', # An improved version of stochastic gradient descent. loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) # Fitting model.fit(training_images, training_labels, epochs=5)

model.evaluate(test_images, test_labels)

import pandas as pd classifications = model.predict(test_images) results = [] for i in range(len(test_labels)): if classifications[i].argmax() == test_labels[i]: results.append(1) else: results.append(0) results_pd = pd.DataFrame({'Results': results}) results_pd.hist(bins=2)

# Callback class class myCallback(tf.keras.callbacks.Callback): def on_epoch_end(self,epoch, logs={}): if(logs.get('accuracy') > 0.95): print("\nReached 95% accuracy. Cancelling training!") self.model.stop_training = True

callbacks=myCallback() model.fit( training_images, training_labels, epochs=50, callbacks=[callbacks] )

model.evaluate(test_images, test_labels)