I have trained my model and it works fine. Then I went on to predict single images (jpg). This also works, but I don't get the exact probabilities now.
This is my model:
def train():
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#nomalize data
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)
#train model
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3)
model.save('Mnist')
print("Done with training :)")
And this is how I predict my single image:
def predict(testImg):
import numpy as np
model = load_model('Mnist')
img = testImg.convert('L').resize((28,28), Image.ANTIALIAS)
img = np.array(img)
predictions = model.predict(img[None,:,:])
I am suspecting it has something to do with the img[None,:,:] reshaping because the predict function was giving back the probabilities with my test set.
Now I am just getting back an array like [0,0,0,0,0,1,0,0,0] and not actual probabilities.