chrisdinant

y_pred = TimeDistributed(Dense(output_dim, activation = 'softmax'))(X) 

# ctc 
y_true = Input(name='the_labels', shape=[None,], dtype='int32') 
input_length = Input(name='input_length', shape=[1], dtype='int32') 
label_length = Input(name='label_length', shape=[1], dtype='int32') 

# Keras doesn't currently support loss funcs with extra parameters 
# so CTC loss is implemented in a lambda layer 
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), 

chrisdinant

x = Reshape((8,1280))(x) 
x = TimeDistributed(Dense(512))(x) 
x_RNN = LSTM(256)(x)

chrisdinant

filters = [32,64,128] input_img = Input(shape = (61,75,1))

def block(filters, inp): 
  inp = inp layer_1 = BatchNormalization()(inp) 
  act_1 = Activation('relu')(layer_1) 
  conv_1 = Conv2D(filters, (3,3), padding = 'same')(act_1) 
  layer_2 = BatchNormalization()(conv_1) 
  act_2 = Activation('relu')(layer_2) 
  conv_2 = Conv2D(filters, (3,3), padding = 'same')(act_2) 
  return(conv_2)