Why is my accuracy high from the beginning of training? - deep-learning

Question

I am training a neural network to recognize some of attributes on .png pictures, and what I get when I start training is something like this, and it is increasing till the end of the epoch:
32/4817 [..............................] - ETA: 167s - loss: 0.6756 - acc: 0.5
64/4817 [..............................] - ETA: 152s - loss: 0.6214 - acc: 0.7
96/4817 [..............................] - ETA: 145s - loss: 0.6169 - acc: 0.7
128/4817 [.............................] - ETA: 142s - loss: 0.5972 - acc: 0.7
160/4817 [.............................] - ETA: 140s - loss: 0.5734 - acc: 0.7
192/4817 [>............................] - ETA: 138s - loss: 0.5604 - acc: 0.7
224/4817 [>............................] - ETA: 137s - loss: 0.5427 - acc: 0.7
256/4817 [>............................] - ETA: 135s - loss: 0.5160 - acc: 0.7
288/4817 [>............................] - ETA: 134s - loss: 0.5492 - acc: 0.7
320/4817 [>............................] - ETA: 133s - loss: 0.5574 - acc: 0.7
352/4817 [=>...........................] - ETA: 131s - loss: 0.5559 - acc: 0.7
384/4817 [=>...........................] - ETA: 129s - loss: 0.5550 - acc: 0.7
416/4817 [=>...........................] - ETA: 128s - loss: 0.5504 - acc: 0.7
448/4817 [=>...........................] - ETA: 127s - loss: 0.5417 - acc: 0.7
480/4817 [=>...........................] - ETA: 126s - loss: 0.5425 - acc: 0.7
My question is why is the starting accuracy so high? I suppose it should be something around 0.1 and then increasing while learning.
Also, at the end I get:
('Test loss:', 0.42451223436727564)
('Test accuracy:', 0.82572614112830256)
Is that too big test loss?
This is my network:
input_shape = x_train[0].shape
print(input_shape)
model = Sequential()
stoplearn = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0,
patience=0, verbose=0, mode='auto')
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=20,
verbose=1,
validation_data=(x_test, y_test),
callbacks=[stoplearn])
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
It is written in Python using Keras.

Answer 1

You classify your data into two classes (since your output layer is of size 2), so accuracy of 0.5 is not high. In fact, it means that your network behaves randomly, which is what you expect at the beginning. Regarding the loss, there is no absolute answer for that. Your test accuracy seems not bad, and you could try to play with some of the parameters (for example, taking smaller size for the fully connected layer) to see whether you can improve it.

Answer 2

You have two classes. Random choice will lead to 50% accuracy. This is what you get in the beginning. Hence your result is expected.
The reason why it jumps directly to 70% accuracy could be that your problem is simple.
If you want to double-check it, you could
use other classifiers,
check how many examples are used to calculate accuracy,
serialize the trained classifier and manually feed it with new examples and check their results