Why does a cnn with keras not learn? - tensorflow

Question

I am kind of new to deep learning and especially keras, and I have an assignment from university to train a CNN and learn about it, using keras. I am using the MURA dataset (skeletonal radiography).
What I have done until now is to go over all images from the dataset and split the training set into train and validation (90/10).
I am using a CNN that has been given in the paper and I am not allowed to modify it until the second task. The first task is to observe and understand the CNN.
def run():
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory('train_data',
target_size=(227,227),
batch_size=BATCH_SIZE,
class_mode='binary',
color_mode='grayscale'
)
val_generator = val_datagen.flow_from_directory('test_data',
target_size=(227,227),
batch_size=BATCH_SIZE,
class_mode='binary',
color_mode='grayscale'
)
classifier = Sequential()
classifier.add(Conv2D(64,(7,7),strides=2, input_shape=(227,227,1)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=2))
classifier.add(Conv2D(128, (5,5), strides=2 ))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2,2),strides=2))
classifier.add(Conv2D(256, (3,3), strides=1))
classifier.add(Activation('relu'))
classifier.add(Conv2D(384, (3,3), strides=1))
classifier.add(Activation('relu'))
classifier.add(Conv2D(256, (3,3), strides=1))
classifier.add(Activation('relu'))
classifier.add(Conv2D(256, (3,3), strides=1))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size=(2,2),strides=2))
classifier.add(Flatten())
classifier.add(Dropout(0.5))
classifier.add(Dense(units=2048))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units=1))
classifier.add(Activation('sigmoid'))
classifier.summary()
# from keras.optimizers import SGD
# sg = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
classifier.compile(optimizer=keras.optimizers.SGD(),loss='binary_crossentropy', metrics=['accuracy'])
classifier.fit_generator(train_generator,
steps_per_epoch= training_len//BATCH_SIZE,
epochs=10,
validation_data=val_generator,
validation_steps= valid_len//BATCH_SIZE,
shuffle=True,
verbose=1)
classifier.save_weights('first_model_weights.h5')
classifier.save('first_model.h5')
The problem I am having is that if I run this, it just does not learn. Or at least I think it doesn't.
The output looks like this:
Epoch 1/10
575/575 [==============================] - 693s 1s/step - loss: 0.6767 - acc: 0.5958 - val_loss: 0.6751 - val_acc: 0.5966
Epoch 2/10
575/575 [==============================] - 207s 359ms/step - loss: 0.6760 - acc: 0.5948 - val_loss: 0.6752 - val_acc: 0.5958
Epoch 3/10
575/575 [==============================] - 258s 448ms/step - loss: 0.6745 - acc: 0.5983 - val_loss: 0.6748 - val_acc: 0.5958
Epoch 4/10
575/575 [==============================] - 165s 287ms/step - loss: 0.6760 - acc: 0.5950 - val_loss: 0.6757 - val_acc: 0.5947
Epoch 5/10
575/575 [==============================] - 166s 288ms/step - loss: 0.6761 - acc: 0.5948 - val_loss: 0.6731 - val_acc: 0.6016
Epoch 6/10
575/575 [==============================] - 167s 290ms/step - loss: 0.6742 - acc: 0.5990 - val_loss: 0.6778 - val_acc: 0.5875
Epoch 7/10
575/575 [==============================] - 206s 359ms/step - loss: 0.6762 - acc: 0.5938 - val_loss: 0.6721 - val_acc: 0.6038
Epoch 8/10
575/575 [==============================] - 165s 286ms/step - loss: 0.6762 - acc: 0.5938 - val_loss: 0.6763 - val_acc: 0.5947
Epoch 9/10
575/575 [==============================] - 164s 286ms/step - loss: 0.6751 - acc: 0.5972 - val_loss: 0.6787 - val_acc: 0.5897
Epoch 10/10
575/575 [==============================] - 168s 292ms/step - loss: 0.6750 - acc: 0.5971 - val_loss: 0.6722 - val_acc: 0.6022
Am I doing something wrong in the code? Is it the dataset splitting? I am currently in a dark spot and I can't seem to figure it out.