Keras: Confusion matrix at every epoch

Simply, just pass the following functions to model.compile function:

from keras import backend as K

def recall_m(y_true, y_pred): # TPR
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) # TP
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) # P
    recall = true_positives / (possible_positives + K.epsilon())
    return recall

def precision_m(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) # TP
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) # TP + FP
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

def f1_m(y_true, y_pred):
    precision = precision_m(y_true, y_pred)
    recall = recall_m(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))

def TP(y_true, y_pred):
    tp = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) # TP
    y_pos = K.round(K.clip(y_true, 0, 1))
    n_pos = K.sum(y_pos)
    y_neg = 1 - y_pos
    n_neg = K.sum(y_neg)
    n = n_pos + n_neg
    return tp/n

def TN(y_true, y_pred):
    y_pos = K.round(K.clip(y_true, 0, 1))
    n_pos = K.sum(y_pos)
    y_neg = 1 - y_pos
    n_neg = K.sum(y_neg)
    n = n_pos + n_neg
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pred_neg = 1 - y_pred_pos
    tn = K.sum(K.round(K.clip(y_neg * y_pred_neg, 0, 1))) # TN
    return tn/n

def FP(y_true, y_pred):
    y_pos = K.round(K.clip(y_true, 0, 1))
    n_pos = K.sum(y_pos)
    y_neg = 1 - y_pos
    n_neg = K.sum(y_neg)
    n = n_pos + n_neg
    tn = K.sum(K.round(K.clip(y_neg * y_pred, 0, 1))) # FP
    return tn/n

def FN(y_true, y_pred):
    y_pos = K.round(K.clip(y_true, 0, 1))
    n_pos = K.sum(y_pos)
    y_neg = 1 - y_pos
    n_neg = K.sum(y_neg)
    n = n_pos + n_neg
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pred_neg = 1 - y_pred_pos
    tn = K.sum(K.round(K.clip(y_true * y_pred_neg, 0, 1))) # FN
    return tn/n

Then,

model.compile(loss='binary_crossentropy',
          optimizer=optimizers.RMSprop(lr=lr),
          metrics=['accuracy',f1_m,precision_m, recall_m, TP, TN, FP, FN])