<jupyter_start><jupyter_text>first try (with one fully connected layer)<jupyter_code>train_labels = tf.keras.utils.to_categorical(train_labels)
valid_dataset = valid_dataset.reshape(valid_dataset.shape[0], 1024)
valid_labels = tf.keras.utils.to_categorical(valid_labels)
train_dataset = train_dataset.reshape(train_dataset.shape[0], 1024)
test_dataset = test_dataset.reshape(test_dataset.shape[0], 1024)
test_labels = tf.keras.utils.to_categorical(test_labels)
model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(1024, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(46, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_dataset, train_labels,validation_data=(valid_dataset, valid_labels), epochs=5,batch_size=128)

model.summary()
score,acc = model.evaluate(test_dataset,test_labels,batch_size=128)
print('Test score:', score)
print('Test accuracy:', acc)<jupyter_output>Test score: 0.22754272053207175
Test accuracy: 0.936594202760337
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