How to use a different CNN without losing accuracy

I have been given a task to implement a Convolutional neural network that can evaluate hand-written digits found in the MNIST dataset with the architecture of the network looking like this:

I have implemented a CNN that matches the architecture, unfortunately it only has about a 10% accuracy to it. I've looked online and tried other example CNNs to make sure if anything else causing the issue, however they seem to work fine and give me a ~99% accuracy. I've placed both CNNs in my code and made a boolean switch to show the difference between the two:

import tensorflow
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D

batch_size = 128
num_classes = 10
epochs = 1
img_rows, img_cols = 28, 28


(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)

exampleModel = False  # Use to toggle which CNN goes into the model

if exampleModel:  # An example CNN that I found for MNIST
    model = Sequential()
    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(num_classes, activation='softmax'))
else:  # The CNN I created
    input_layer = tensorflow.keras.layers.Input(shape=input_shape)
    conv1 = Conv2D(32, (1, 1), activation='relu')(input_layer)
    pool1 = MaxPooling2D(2, 2)(conv1)
    conv2_1 = Conv2D(64, (1, 1), activation='relu', padding='same')(pool1)
    pool2_1 = MaxPooling2D(2, 2)(conv2_1)
    drop2_1 = Dropout(0.5)(pool2_1)
    conv2_2 = Conv2D(64, (1, 1), activation='relu', padding='same')(pool1)
    pool2_2 = MaxPooling2D(2, 2)(conv2_2)
    drop2_2 = Dropout(0.5)(pool2_2)
    conv3_1 = Conv2D(256, (1, 1), activation='relu', padding='same')(drop2_1)
    conv3_2 = Conv2D(256, (1, 1), activation='relu', padding='same')(drop2_2)
    merged = tensorflow.keras.layers.concatenate([conv3_1, conv3_2], axis=-1)
    merged = Dropout(0.5)(merged)
    merged = Flatten()(merged)
    fc1 = Dense(1000, activation='relu')(merged)
    fc2 = Dense(500, activation='relu')(fc1)
    out = Dense(10)(fc2)
    model = tensorflow.keras.models.Model(input_layer, out)

model.compile(loss=tensorflow.keras.losses.categorical_crossentropy,
              optimizer=tensorflow.keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

In order to complete my task, I believe I have to convert my example CNN piece-by-piece into the required architecture. Although I have no idea how to do this, they look completely different from each other (one is purely sequential, the other uses parallel layers and merging).

You simply have to add an softmax activation to the last, out layer:

out = Dense(10, activation="softmax")(fc2)

Thus your model in completed form:

input_layer = tensorflow.keras.layers.Input(shape=input_shape)
conv1 = Conv2D(32, (1, 1), activation='relu')(input_layer)
pool1 = MaxPooling2D(2, 2)(conv1)
conv2_1 = Conv2D(64, (1, 1), activation='relu', padding='same')(pool1)
pool2_1 = MaxPooling2D(2, 2)(conv2_1)
drop2_1 = Dropout(0.5)(pool2_1)
conv2_2 = Conv2D(64, (1, 1), activation='relu', padding='same')(pool1)
pool2_2 = MaxPooling2D(2, 2)(conv2_2)
drop2_2 = Dropout(0.5)(pool2_2)
conv3_1 = Conv2D(256, (1, 1), activation='relu', padding='same')(drop2_1)
conv3_2 = Conv2D(256, (1, 1), activation='relu', padding='same')(drop2_2)
merged = tensorflow.keras.layers.concatenate([conv3_1, conv3_2], axis=-1)
merged = Dropout(0.5)(merged)
merged = Flatten()(merged)
fc1 = Dense(1000, activation='relu')(merged)
fc2 = Dense(500, activation='relu')(fc1)
out = Dense(10, activation="softmax")(fc2)

Out:

x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Train on 60000 samples, validate on 10000 samples
Epoch 1/1
60000/60000 [==============================] - 25s 416us/step - loss: 0.6394 - acc: 0.7858 - val_loss: 0.2956 - val_acc: 0.9047
Test loss: 0.29562548571825026
Test accuracy: 0.9047