DiegoAgher

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Total params: 78,519
Trainable params: 78,069
Non-trainable params: 450

DiegoAgher

seq_length = 9
random_chars = np.random.randint(0, len(x_train)-1, seq_length)
image_seq = []
for j in random_chars:
 if np.random.random(1) >= 0.5:
 image_seq.append(cv2.medianBlur(x_train[j], 5))
 else:
 image_seq.append(x_train[j])
image_seq = np.concatenate(image_seq, axis=1)

DiegoAgher

y_pred = Dense(num_classes, activation='softmax',
               kernel_initializer='he_normal',name='output')(lstm_2)

DiegoAgher

lstm_1 = LSTM(32, return_sequences=True, kernel_initializer='he_normal', name='lstm1')(reshape)
lstm_2 = LSTM(32, return_sequences=True, kernel_initializer='he_normal', name='lstm2')(lstm_1)

DiegoAgher

conv1 = Conv2D(seq_lenght, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)
…
dims = conv1.get_shape()
reshape = Reshape(target_shape=(seq_lenght, int(dims[1]*dims[2])), name='reshape')(conv1)

DiegoAgher

def build_Model(num_classes, seq_lenght, input_shape=(28, 252, 1)):
    inputs = Input(name='x', shape=input_shape, dtype='float32')

    conv1 = Conv2D(seq_lenght, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)
    conv1 = Activation('relu')(conv1)
    conv1 = MaxPooling2D(pool_size=(2, 2), name='max1')(conv1)
    
    dims = conv1.get_shape()
    
    reshape = Reshape(target_shape=(seq_lenght, int(dims[1]*dims[2])), name='reshape')(conv1)

DiegoAgher

plt.imshow(image_seq)

DiegoAgher

seq_length = 9
random_chars = np.random.randint(0, len(x_train)-1, seq_length)
image_seq = np.concatenate([x_train[j] for j in random_chars], axis=1)
seq_label = np.array([y_train[j] for j in random_chars])

DiegoAgher

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

DiegoAgher

### Keybase proof

I hereby claim:

  * I am diegoagher on github.
  * I am agher (https://keybase.io/agher) on keybase.
  * I have a public key ASBfCDf3tTF7YMTy4Ut1T13OfCHuZLcE-Mz0MTKzrZs7Rwo

To claim this, I am signing this object: