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Why is training-set accuracy during fit() different to accuracy calculated right after using predict on same data?

Have written a basic deep learning model in Tensorflow - Keras.
Why is the training-set accuracy as reported at the end of training (0.4097) differs to that reported directly afterwards with a direct calculation on the same training data using the predict function (or using evaluate, which gives the same number) = 0.6463?
MWE below; output directly after.
from extra_keras_datasets import kmnist
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
import numpy as np
# Model configuration
no_classes = 10
# Load KMNIST dataset
(input_train, target_train), (input_test, target_test) = kmnist.load_data(type='kmnist')
# Shape of the input sets
input_train_shape = input_train.shape
input_test_shape = input_test.shape
# Keras layer input shape
input_shape = (input_train_shape[1], input_train_shape[2], 1)
# Reshape the training data to include channels
input_train = input_train.reshape(input_train_shape[0], input_train_shape[1], input_train_shape[2], 1)
input_test = input_test.reshape(input_test_shape[0], input_test_shape[1], input_test_shape[2], 1)
# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')
# Normalize input data
input_train = input_train / 255
input_test = input_test / 255
# Create the model
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(no_classes, activation='softmax'))
# Compile the model
model.compile(loss=tensorflow.keras.losses.sparse_categorical_crossentropy,
optimizer=tensorflow.keras.optimizers.Adam(),
metrics=['accuracy'])
# Fit data to model
history = model.fit(input_train, target_train,
batch_size=2000,
epochs=1,
verbose=1)
prediction = model.predict(input_train)
print("Prediction accuracy = ", np.mean( np.argmax(prediction, axis=1) == target_train))
model.evaluate(input_train, target_train, verbose=2)
Last couple of lines of output:
30/30 [==============================] - 0s 3ms/step - loss: 1.8336 - accuracy: 0.4097
Prediction accuracy = 0.6463166666666667
1875/1875 - 1s - loss: 1.3406 - accuracy: 0.6463
Edit.
The initial answers below have solved my first problem by pointing out that the batch size matters when you only run 1 epoch. When running small batch sizes (or batch size = 1), or more epochs, you can push the post-fitting prediction accuracy pretty close to the final accuracy thrown out in fitting itself. Which is good!
I originally asked this question because I was having trouble with a more complex model.
I'm still have trouble with understanding what's happening in this case (and yes, it involves batch normalisation). To get my MWE, replace everything below 'create the model' above with the code below to implement a few fully connected layers with batch normalisation.
When you run two epochs of this - you'll see really stable accuracies from all 30 mini-batches (30 because 60,000 in training set divided by 2000 in each batch). I see very consistently 83% accuracy across the whole second epoch of training.
But the prediction after fitting is an abysmal 10% or thereabouts after doing this. Can anyone explain this?
model = Sequential()
model.add(Dense(50, activation='relu', input_shape = input_shape))
model.add(BatchNormalization())
model.add(Dense(20, activation='relu'))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(no_classes, activation='softmax'))
# Compile the model
model.compile(loss=tensorflow.keras.losses.sparse_categorical_crossentropy,
optimizer=tensorflow.keras.optimizers.Adam(),
metrics=['accuracy'])
# Fit data to model
history = model.fit(input_train, target_train,
batch_size=2000,
epochs=2,
verbose=1)
prediction = model.predict(input_train)
print("Prediction accuracy = ", np.mean( np.argmax(prediction, axis=1) == target_train))
model.evaluate(input_train, target_train, verbose=2, batch_size = batch_size)
30/30 [==============================] - 46s 2s/step - loss: 0.5567 - accuracy: 0.8345
Prediction accuracy = 0.10098333333333333

One reason this can happen, is the last accuracy reported takes into account the entire epoch, with its parameters non constant, and still being optimized.
When evaluating the model, the parameters stop changing, and they remain in their final (hopefully, most optimized) state. Unlike during the last epoch, for which the parameters were in all kinds of (hopefully, less optimized) states, more so at the start of the epoch.
Deleted because I now see you didn't use batch norm in this case.
I am assuming this is due to BatchNormalization.
See for example here
During training, a moving average is used.
During inference, we already have the normalization parameters
This is likely to be the cause of the difference.
Please try without it, and see if still such drastic differences exist.

Just adding to #Gulzar answer: this effect can be very clear because OP used only one epoch (a lot of parameters are changing in the very beginning of trainning), batch size not equal in evaluate method (default to 32) and fit method, batch size lot less than whole data (meaning a lot of updating during each epoch).
Just adding more epochs to same experiment would attenuate this effect.
# Fit data to model
history = model.fit(input_train, target_train,
batch_size=2000,
epochs=40,
verbose=1)
Result
Epoch 40/40
30/30 [==============================] - 0s 11ms/step - loss: 0.5663 - accuracy: 0.8339
Prediction accuracy = 0.8348
1875/1875 - 2s - loss: 0.5643 - accuracy: 0.8348 - 2s/epoch - 1ms/step
[0.5643048882484436, 0.8348000049591064]

Tensorflow model not improving

I'm just starting learning ML/Tensorflow/etc, so I'm pretty novice and still don't really know what the troubleshooting method is like. I'm currently having an issue with my model as it doesn't seem to really ever improve. For instance, the output appears as
Epoch 1/10
4/4 [==============================] - 41s 10s/step - loss: 0.8833 - accuracy: 0.4300
Epoch 2/10
4/4 [==============================] - 12s 3s/step - loss: 0.8833 - accuracy: 0.4300
Epoch 3/10
4/4 [==============================] - 10s 3s/step - loss: 0.8833 - accuracy: 0.4300
Epoch 7/1000
4/4 [==============================] - 10s 3s/step - loss: 0.8833 - accuracy: 0.4300
The main aspect that worries me is that it doesn't change at all which makes me think I'm doing something completely wrong. To give some more context and code, I am trying to do some time series classification. Basically, the input is the normalized time series of the song and the net should classify if it's classical music (output of 1 means it is, output of 0 means it isn't).
This is the current model I am trying.
model = keras.Sequential([
keras.layers.Conv1D(filters=100, kernel_size=10000, strides=5000, input_shape=(1323000, 1), activation='relu'),
keras.layers.Conv1D(filters=100, kernel_size=10, strides=3, input_shape=(263, 100), activation='relu'),
keras.layers.LSTM(1000),
keras.layers.Dense(500, activation='relu'),
keras.layers.Dense(250, activation='relu'),
keras.layers.Dense(1, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
This is how I get the training data (x and y are dictionaries with time series from different songs).
minute = 1323000
x_train = np.zeros((100, minute, 1))
y_train = np.zeros((100,))
for kk in range(0, 100):
num = randint(0, 41)
ts = x[num]
start = randint(0, len(ts) - minute)
x_train[kk, :] = np.array([ts[start:(start + minute)]]).T
y_train[kk] = 1 - y[num]
and then training:
for kk in range(1, 1000):
x_train, y_train = create_training_set(x, y)
model.fit(x_train, y_train, epochs=1000)
I looked at some similar questions asked, however, I was already doing what was suggested or the advice was too specific for the asker. I also tried some relatively different models/activators, so I don't think it's because the model is too complicated and the data is already normalized, so that shouldn't be an issue. But, as I said, I'm knew to all this and could be wrong.

The usage of softmax activation in a single-node last layer is not correct. Additionally, the argument from_logits=True in your loss definition means that the model is expected to produce logits, and not probabilities (which are generally produced by softmax and sigmoid final activations).
So, you should change your last layer to
keras.layers.Dense(1) # linear activation by default
Alternatively, you could change both your last layer and your loss function respectively to
keras.layers.Dense(1, activation='sigmoid')
loss=tf.keras.losses.BinaryCrossentropy(from_logits=False)
According to the docs, usage of from_logits=True may be more numerically stable, and probably this is the reason it is preferred in the standard Tensorflow classification tutorials (see here and here).

So I can't promise this is going to work, because I don't know your data and this is a very weird architecture, but here are a few things that seem wrong:
Last dense layer should have sigmoid activation function
from_logits should be False

Big difference in accuracy after training the model and after loading that model

I made Keras NN model for fake news detection. My features are avg length of the words, avg length of the sentence, number of punctuation signs, number of capital words, number of questions etc. I have 34 features. I have one output, 0 and 1 (0 for fake and 1 for real news).
I have used 50000 samples for training, 10000 for testing and 2000 for validation. Values of my data are going from -1 to 10, so there is not big difference between values. I have used Standard Scaler like this:
x_train, x_test, y_train, y_test = train_test_split(features, results, test_size=0.20, random_state=0)
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
validation_features = scaler.transform(validation_features)
My NN:
model = Sequential()
model.add(Dense(34, input_dim = x_train.shape[1], activation = 'relu')) # input layer requires input_dim param
model.add(Dense(150, activation = 'relu'))
model.add(Dense(150, activation = 'relu'))
model.add(Dense(150, activation = 'relu'))
model.add(Dense(150, activation = 'relu'))
model.add(Dense(150, activation = 'relu'))
model.add(Dense(1, activation='sigmoid')) # sigmoid instead of relu for final probability between 0 and 1
model.compile(loss="binary_crossentropy", optimizer= "adam", metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', min_delta=0.0, patience=0, verbose=0, mode='auto')
model.fit(x_train, y_train, epochs = 15, shuffle = True, batch_size=64, validation_data=(validation_features, validation_results), verbose=2, callbacks=[es])
scores = model.evaluate(x_test, y_test)
print(model.metrics_names[0], round(scores[0]*100,2), model.metrics_names[1], round(scores[1]*100,2))
Results:
Train on 50407 samples, validate on 2000 samples
Epoch 1/15
- 3s - loss: 0.3293 - acc: 0.8587 - val_loss: 0.2826 - val_acc: 0.8725
Epoch 2/15
- 1s - loss: 0.2647 - acc: 0.8807 - val_loss: 0.2629 - val_acc: 0.8745
Epoch 3/15
- 1s - loss: 0.2459 - acc: 0.8885 - val_loss: 0.2602 - val_acc: 0.8825
Epoch 4/15
- 1s - loss: 0.2375 - acc: 0.8930 - val_loss: 0.2524 - val_acc: 0.8870
Epoch 5/15
- 1s - loss: 0.2291 - acc: 0.8960 - val_loss: 0.2423 - val_acc: 0.8905
Epoch 6/15
- 1s - loss: 0.2229 - acc: 0.8976 - val_loss: 0.2495 - val_acc: 0.8870
12602/12602 [==============================] - 0s 21us/step
loss 23.95 acc 88.81
Accuracy check:
prediction = model.predict(validation_features , batch_size=64)
res = []
for p in prediction:
res.append(p[0].round(0))
# Accuracy with sklearn
acc_score = accuracy_score(validation_results, res)
print("Sklearn acc", acc_score) # 0.887
Saving the model:
model.save("new keras fake news acc 88.7.h5")
scaler_filename = "keras nn scaler.save"
joblib.dump(scaler, scaler_filename)
I have saved that model and that scaler.
When I load that model and that scaler, and when I want to make prediction, I get accuracy of 52%, and thats very low because I had accuracy of 88.7% when I was training that model.
I applied .transform on my new data for testing.
validation_df = pd.read_csv("validation.csv")
validation_features = validation_df.iloc[:,:-1]
validation_results = validation_df.iloc[:,-1].tolist()
scaler = joblib.load("keras nn scaler.save")
validation_features = scaler.transform(validation_features)
my_model_1 = load_model("new keras fake news acc 88.7.h5")
prediction = my_model_1.predict(validation_features , batch_size=64)
res = []
for p in prediction:
res.append(p[0].round(0))
# Accuracy with sklearn - much lower
acc_score = accuracy_score(validation_results, res)
print("Sklearn acc", round(acc_score,2)) # 0.52
Can you tell me what am I doing wrong, I have read a lot about this on github and stackoverflow but I couldnt find the answer?

It is difficult to answer that without your actual data. But there is a smoking gun, raising suspicions that your validation data might be (very) different from your training & test ones; and it comes from your previous question on this:
If i use fit_transform on my [validation set] features, I do not get an error, but I get accuracy of 52%, and that's terrible (because I had 89.1 %).
Although using fit_transform on the validation data is indeed wrong methodology (the correct one being what you do here), in practice, it should not lead to such a high discrepancy in the accuracy.
In other words, I have actually seen many cases where people erroneously apply such fit_transform approaches on their validation/deployment data, without never realizing any mistake in it, simply because they don't get any performance discrepancy - hence they are not alerted. And such a situation is expected, if indeed all these data are qualitatively similar.
But discrepancies such as yours here lead to strong suspicions that your validation data are actually (very) different from your training & test ones. If this is the case, such performance discrepancies are to be expected: the whole ML practice is founded upon the (often implicit) assumption that our data (training, validation, test, real-world deployment ones etc) do not change qualitatively, and they all come from the same statistical distribution.
So, the next step here is to perform an exploratory analysis to both your training & validation data to investigate this (actually, this is always assumed to be the step #0 in any predictive task). I guess that even elementary measures (mean & max/min values etc) will show if there are strong differences between them, as I suspect.
In particular, scikit-learn's StandardScaler uses
z = (x - u) / s
for the transformation, where u is the mean value and s the standard deviation of the data. If these values are significantly different between your training and validation sets, the performance discrepancy is not to be unexpected.

Accuracy metric fails with trivial custom Keras loss function

I am playing around with custom loss functions on Keras models. My "custom" loss seems to fail (in terms of accuracy score), even though I am only using a wrapper that returns an original keras loss.
As a toy example, I am using the "Basic classification" Tensorflow/Keras tutorial that uses a simple NN on the fashion-MNIST data set and I am following the related Keras documentation and this SO post.
This is the model:
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
Now, if I leave the sparse_categorical_crossentropy as a string argument in compile() function, the training results to a ~ 87% accuracy which is fine:
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
But when I just create a trivial wrapper function to call keras' cross-entropy I get a ~ 10% accuracy both on training and test sets:
from tensorflow.keras import losses
def my_loss(y_true, y_pred):
return losses.sparse_categorical_crossentropy(y_true, y_pred)
model.compile(optimizer='adam',
loss=my_loss,
metrics=['accuracy'])
Epoch 1/10 60000/60000 [==============================] - 3s 51us/sample - loss: 0.5030 - accuracy: 0.1032
Epoch 2/10 60000/60000 [==============================] - 3s 45us/sample - loss: 0.3766 - accuracy: 0.1035
...
Test accuracy: 0.1013
By plotting a few images and checking their classified labels, it doesn't look like the results differ in each case, but accuracies printed are very different. So, is it the case that the default metrics do not play nicely with custom losses? Can it be the case that what I see is the error rather than the accuracy? Am I missing something from the documentation?
Edit: The values of the loss functions in both cases end up roughly the same, so training indeed takes place. The accuracy is the point of failure.

Here's the reason:
When you use inbuilt loss and use loss='sparse_categorical_crossentropy' at that time accuracy metric used is sparse_categorical_accuracy But when you use custom loss function at that time accuracy metric used is categorical_accuracy.
Example:
model.compile(optimizer='adam',
loss=losses.sparse_categorical_crossentropy,
metrics=['categorical_accuracy', 'sparse_categorical_accuracy'])
model.fit(train_images, train_labels, epochs=1)
'''
Train on 60000 samples
60000/60000 [==============================] - 5s 86us/sample - loss: 0.4955 - categorical_accuracy: 0.1045 - sparse_categorical_accuracy: 0.8255
'''
model.compile(optimizer='adam',
loss=my_loss,
metrics=['accuracy', 'sparse_categorical_accuracy'])
model.fit(train_images, train_labels, epochs=1)
'''
Train on 60000 samples
60000/60000 [==============================] - 5s 87us/sample - loss: 0.4956 - acc: 0.1043 - sparse_categorical_accuracy: 0.8256
'''

Mapping NN Output of Keras CNN in Python to interval [0;1]

I attempt to train a CNN to binary classify images of the (maybe uncommon) shape of height=2 and width=1000 pixels. My first approach is a small and simple CNN coded as follows:
def cnn_model_01():
model = Sequential()
# Assembly of layers
model.add(Conv2D(16, (2, 2), input_shape=(1, 2, 1000), activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 1)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compilation of model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = cnn_model_01()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200, verbose=2)
The accuracy and prediction of the NN results in a value which reflects simply the distribution of values in the sample. Typical training output is
13s - loss: 0.7772 - acc: 0.5680 - val_loss: 0.6657 - val_acc: 0.6048
Epoch 2/5
15s - loss: 0.6654 - acc: 0.5952 - val_loss: 0.6552 - val_acc: 0.6048
Epoch 3/5
15s - loss: 0.6514 - acc: 0.5952 - val_loss: 0.6396 - val_acc: 0.6048
Epoch 4/5
15s - loss: 0.6294 - acc: 0.5952 - val_loss: 0.6100 - val_acc: 0.6048
Epoch 5/5
13s - loss: 0.5933 - acc: 0.6116 - val_loss: 0.5660 - val_acc: 0.6052
The reason for this is that the NN assigns all input samples to one class. So, in approximately two thirds it is correct by chance in the case of a sample distributed in exactly this way.
In order to fix the problem and get the NN to produce better results I've inspected the output and encountered that the interval or domain of these values is relatively small, e.g. between [0.55;0.62]. I've tried to map resp. resize this interval to [0;1]. As a result a got a really good accuracy of ~99%. I've done this mapping "by hand": subtract the minimum value of the array from each value and divide it by the difference of the maximum and minimum.
Can I implement this mapping in Keras? Is there a layer with this functionality?
Or did I do something completely wrong/not advisable with the layers, which leads to this narrow interval of the output?

I'm not sure I entirely understand what you want to achieve.
But I have three ideas out of which one or two may help you.
1) Add a Dense(2) layer before the output layer and change the activation of the output layer to softmax. That way you'd have the previous layer classify the image as class 1 or class 2. The last Dense(1) layer would then "merge" that information into a single value 0 or 1 as output.
2) I assume you could pick a threshold, e.g. 0.5 and simply compare the probability-based output of your NN and so something like result = output > 0.5. This could also be done inside a Lambda layer, i.e. model.add(Lambda(lambda x: 1 if x > 0.5 else 0))
3) When predicting, you can use predict_class instead of predict and get 0 or 1 as result instead of probabilities. This resembles my previous suggestion 2.
I hope one of the suggestions matches your task.