Keras fit_generator() not working due to shape error - keras

Question

I am running MNIST prediction using Keras, with tensorflow backend.
I have code that runs with batches , using Keras fit() as
(X_train, y_train), (X_test, y_test) = mnist.load_data()
N1 = X_train.shape[0]
N2 = X_test.shape[0]
h = X_train.shape[1]
w = X_train.shape[2]
num_pixels = h*w
# reshape N1 samples to num_pixels
x_train = X_train.reshape(N1, num_pixels).astype('float32') # shape is now (60000,784)
x_test = X_test.reshape(N2, num_pixels).astype('float32') # shape is now (10000,784)
x_train = x_train / 255
x_test = x_test / 255
y_train = np_utils.to_categorical(y_train) #(60000,10)
y_test = np_utils.to_categorical(y_test) # (10000,10):
num_classes = y_test.shape[1]
def baseline_model():
# create model
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = baseline_model()
batch_size = 200
epochs = 20
max_batches = 2 * len(x_train) / batch_size # 2*60000/200
# reshape to be [samples][width][height][ channel] for ImageDataGenerator
x_t = X_train.reshape(N1, w, h, 1).astype('float32')
datagen = ImageDataGenerator(rescale= 1./255)
train_gen = datagen.flow(x_t, y_train, batch_size=batch_size)
for e in range(epochs):
batches = 0
for x_batch, y_batch in train_gen:
# x_batch is of size [batch_sz,w,h,ch]: resize to [bth_sz,pixel_sz]: (200,28,28,1)-> (200,784)
# for model.fit
x_batch = np.reshape(x_batch, [-1, num_pixels])
model.fit(x_batch, y_batch,validation_split=0.15,verbose=0)
batches += 1
print("Epoch %d/%d, Batch %d/%d" % (e+1, epochs, batches, max_batches))
if batches >= max_batches:
break
scores = model.evaluate(x_test, y_test, verbose=0)
However, when I try to implement similar code using fit_generator(), I get an error.
the code is as below:
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# separate data into train and validation
from sklearn.model_selection import train_test_split
# Split the data
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.15, shuffle= True)
# number of training samples
N1 = X_train.shape[0] # training size
N2 = X_test.shape[0] # test size
N3 = X_valid.shape[0] # valid size
h = X_train.shape[1]
w = X_train.shape[2]
num_pixels = h*w
y_train = np_utils.to_categorical(y_train)
y_valid = np_utils.to_categorical(y_valid)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
def baseline_model():
# create model
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = baseline_model()
batch_size = 200
epochs = 20
steps_per_epoch_tr = int(N1/ batch_size) # 51000/200
steps_per_epoch_val = int(N3/batch_size)
# reshape to be [samples][width][height][ channel] for ImageData Gnerator->datagen.flow
x_t = X_train.reshape(N1, w, h, 1).astype('float32')
x_v = X_valid.reshape(N3, w, h, 1).astype('float32')
# define data preparation
datagen = ImageDataGenerator(rescale=1./255) # scales x_t/x_v
train_gen = datagen.flow(x_t, y_train, batch_size=batch_size)
valid_gen = datagen.flow(x_v,y_valid, batch_size=batch_size)
model.fit_generator(train_gen,steps_per_epoch = steps_per_epoch_tr,validation_data = valid_gen,
validation_steps = steps_per_epoch_val,epochs=epochs)
This gives an error:
This is due to expected image dimension error, but I am not sure where/how to fix this. any help is greatly appreciated.
Thanks
sedy

Answer 1

In the model.fit() case, this line flattened the input before feeding it for training.
x_batch = np.reshape(x_batch, [-1, num_pixels])
But in the generator case, there is nothing to flatten the input before feeding it to the Dense layer. The Dense layer cannot process 2D input (28 x 28). Adding, a Flatten() layer to the model should do the trick as shown below.
def baseline_model():
# create model
model = Sequential()
model.add(Flatten(input_shape=(28,28,1)))
model.add(Dense(num_pixels, input_dim=num_pixels, kernel_initializer='normal', activation='relu'))
model.add(Dense(num_classes, kernel_initializer='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model