First implemented logistic regression using tensorflow keras low-level api. Then using high-level keras API implemented the same algorithm but loss/cost and accuracies are different. Low-level answer is correct as it matches logistic regression by scratch of deeplearning.ai. Google colab links of files is attached.
below is low-level api logistic regression code which is giving correct output:

## initialize part
print_cost=True
num_epochs = 10
learning_rate = 0.005

w = tf.Variable(tf.zeros(shape=(X.shape[0],1))) # shape=(2,1)
b = tf.Variable(0.0)

optimizer = tf.keras.optimizers.SGD(learning_rate)
accuracy = tf.keras.metrics.BinaryAccuracy()

## training part
for i in range(num_epochs):

    # forward pass
    with tf.GradientTape() as tape:
        z = tf.add(tf.matmul(tf.transpose(w), X), b)
        bce = tf.keras.losses.BinaryCrossentropy( from_logits=True, label_smoothing=0.0, axis=-1, reduction='sum', name='binary_crossentropy')
        cost = bce(Y, z)
    # backward pass
    trainable_variables = [w, b]
    grads = tape.gradient(cost, trainable_variables)
    optimizer.apply_gradients(zip(grads, trainable_variables))

    # Print the cost every 100 training epochs
    if print_cost:
        accuracy.update_state(Y, tf.sigmoid(z))
        print(f"Epoch {i+1}/{num_epochs}  -  Cost:{cost.numpy()}  -  Accuracy:{accuracy.result().numpy()}")
        accuracy.reset_states()

Below is high-level api logistic regression code which is giving incorrect output:

# Create the model
model = k.models.Sequential()
model.add( k.layers.Dense(units=1, activation='sigmoid', input_shape=(X.shape[0],),     #X.shape[0]=2
                          kernel_initializer=k.initializers.Zeros(), bias_initializer=k.initializers.Zeros()) )

# Compile the model
model.compile(
    optimizer=k.optimizers.SGD(learning_rate=0.005),
    loss=k.losses.BinaryCrossentropy(),
    metrics=[k.metrics.BinaryAccuracy()],
)

# Train the model
model.fit(tf.transpose(X), tf.transpose(Y), epochs=10, shuffle=False, batch_size=1)

Unable to correctly implement logistic regression in keras high-level api tensorflow but has successfully implemented in low-level api. Kindly correct my logic in last code snippet.