Input Layer in keras model class gives type-error with numpy array or tensors as input. What is the correct type then?

Question

How to give input to the model if not a numpy array?

def createmodel():
    myInput = Input(shape=(96, 96, 3))

x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)
x = Lambda(LRN2D, name='lrn_1')(x)
x = Conv2D(64, (1, 1), name='conv2')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(192, (3, 3), name='conv3')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
x = Activation('relu')(x)
x = Lambda(LRN2D, name='lrn_2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)

# Inception3a
inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)

inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)

inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
inception_3a_pool = Activation('relu')(inception_3a_pool)
inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)

inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
inception_3a_1x1 = Activation('relu')(inception_3a_1x1)

inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)

# Inception3b
inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)

inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)

inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
inception_3b_pool = Activation('relu')(inception_3b_pool)
inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)

inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
inception_3b_1x1 = Activation('relu')(inception_3b_1x1)

inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)

# Inception3c
inception_3c_3x3 = utils.conv2d_bn(inception_3b,
                                   layer='inception_3c_3x3',
                                   cv1_out=128,
                                   cv1_filter=(1, 1),
                                   cv2_out=256,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(2, 2),
                                   padding=(1, 1))

inception_3c_5x5 = utils.conv2d_bn(inception_3b,
                                   layer='inception_3c_5x5',
                                   cv1_out=32,
                                   cv1_filter=(1, 1),
                                   cv2_out=64,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(2, 2),
                                   padding=(2, 2))

inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)

inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)

#inception 4a
inception_4a_3x3 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=192,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))
inception_4a_5x5 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_5x5',
                                   cv1_out=32,
                                   cv1_filter=(1, 1),
                                   cv2_out=64,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(1, 1),
                                   padding=(2, 2))

inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
inception_4a_pool = utils.conv2d_bn(inception_4a_pool,
                                   layer='inception_4a_pool',
                                   cv1_out=128,
                                   cv1_filter=(1, 1),
                                   padding=(2, 2))
inception_4a_1x1 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))
inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)

#inception4e
inception_4e_3x3 = utils.conv2d_bn(inception_4a,
                                   layer='inception_4e_3x3',
                                   cv1_out=160,
                                   cv1_filter=(1, 1),
                                   cv2_out=256,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(2, 2),
                                   padding=(1, 1))
inception_4e_5x5 = utils.conv2d_bn(inception_4a,
                                   layer='inception_4e_5x5',
                                   cv1_out=64,
                                   cv1_filter=(1, 1),
                                   cv2_out=128,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(2, 2),
                                   padding=(2, 2))
inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)

inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)

#inception5a
inception_5a_3x3 = utils.conv2d_bn(inception_4e,
                                   layer='inception_5a_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=384,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))

inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
inception_5a_pool = utils.conv2d_bn(inception_5a_pool,
                                   layer='inception_5a_pool',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   padding=(1, 1))
inception_5a_1x1 = utils.conv2d_bn(inception_4e,
                                   layer='inception_5a_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))

inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)

#inception_5b
inception_5b_3x3 = utils.conv2d_bn(inception_5a,
                                   layer='inception_5b_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=384,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
inception_5b_pool = utils.conv2d_bn(inception_5b_pool,
                                   layer='inception_5b_pool',
                                   cv1_out=96,
                                   cv1_filter=(1, 1))
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)

inception_5b_1x1 = utils.conv2d_bn(inception_5a,
                                   layer='inception_5b_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)

av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
reshape_layer = Flatten()(av_pool)
dense_layer = Dense(128, name='dense_layer')(reshape_layer)
norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)


# Final Model
return Model(inputs=[myInput], outputs=norm_layer)

Calling the above model as:

from keras import backend as K
from keras.models import Model
from keras.layers import Input, Layer


# Input for anchor, positive and negative images
in_a =np.random.randint(10,100,(1,96,96,3)).astype(float)
in_p =np.random.randint(10,100,(1,96,96,3)).astype(float)
in_n =np.random.randint(10,100,(1,96,96,3)).astype(float)

in_a_a =K.variable(value=in_a)
in_p_p =K.variable(value=in_p)
in_n_n =K.variable(value=in_n)

# # Output for anchor, positive and negative embedding vectors
# # The nn4_small model instance is shared (Siamese network)

emb_a = nn4_small2(in_a_a)
emb_p = nn4_small2(in_p_p)
emb_n = nn4_small2(in_n_n)

class TripletLossLayer(Layer):
    def __init__(self, alpha, **kwargs):
        self.alpha = alpha
        super(TripletLossLayer, self).__init__(**kwargs)

    def triplet_loss(self, inputs):
        a, p, n = inputs
        p_dist = K.sum(K.square(a-p), axis=-1)
        n_dist = K.sum(K.square(a-n), axis=-1)
        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)

    def call(self, inputs):
        loss = self.triplet_loss(inputs)
        self.add_loss(loss)
        return loss

# # # Layer that computes the triplet loss from anchor, positive and negative embedding vectors
triplet_loss_layer = TripletLossLayer(alpha=0.2, name='triplet_loss_layer')([emb_a, emb_p, emb_n])

# # # Model that can be trained with anchor, positive negative images
nn4_small2_train = Model([in_a, in_p, in_n], triplet_loss_layer)

Gives a type error on this line:

nn4_small2_train = Model([in_a, in_p, in_n], triplet_loss_layer)

c:\users\amark\anaconda3\envs\python3.5\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your ' + object_name + 90 ' call to the Keras 2 API: ' +signature,stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper

c:\users\amark\anaconda3\envs\python3.5\lib\site-packages\keras\engine\topology.py in init(self, inputs, outputs, name)
1526
1527 # Check for redundancy in inputs.
-> 1528 if len(set(self.inputs)) != len(self.inputs):
1529 raise ValueError('The list of inputs passed to the model '
1530 'is redundant. '

TypeError: unhashable type: 'numpy.ndarray'

If I try to use the following:

nn4_small2_train = Model([in_a_a, in_p_p, in_n_n], triplet_loss_layer)

then the error raised is:

TypeError: Input tensors to a Model must be Keras tensors. Found: (missing Keras metadata)

Answer 1

You are passing numpy arrays as inputs to build a Model, and that is not right, you should pass instances of Input.

In your specific case, you are passing in_a, in_p, in_n but instead to build a Model you should be giving instances of Input, not K.variables (your in_a_a, in_p_p, in_n_n) or numpy arrays. Also it makes no sense to give values to the varibles. First you build the model symbolically, without any specific input values, and then you can train it or predict on it with real input values.