I am trying to save Nueral Network weights into a file and then restoring those weights by initializing the network instead of random initialization. My code works fine with random initialization. But, when i initialize weights from file it is showing me an error TypeError: Input 'b' of 'MatMul' Op has type float64 that does not match type float32 of argument 'a'. I don't know how do i solve this issue.Here is my code:
Model Initialization
# Parameters training_epochs = 5 batch_size = 64 display_step = 5 batch = tf.Variable(0, trainable=False) regualarization = 0.008 # Network Parameters n_hidden_1 = 300 # 1st layer num features n_hidden_2 = 250 # 2nd layer num features n_input = model.layer1_size # Vector input (sentence shape: 30*10) n_classes = 12 # Sentence Category detection total classes (0-11 categories) #History storing variables for plots loss_history = [] train_acc_history = [] val_acc_history = [] # tf Graph input x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes]) Model parameters
#loading Weights def weight_variable(fan_in, fan_out, filename): stddev = np.sqrt(2.0/fan_in) if (filename == ""): initial = tf.random_normal([fan_in,fan_out], stddev=stddev) else: initial = np.loadtxt(filename) print initial.shape return tf.Variable(initial) #loading Biases def bias_variable(shape, filename): if (filename == ""): initial = tf.constant(0.1, shape=shape) else: initial = np.loadtxt(filename) print initial.shape return tf.Variable(initial) # Create model def multilayer_perceptron(_X, _weights, _biases): layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) return tf.matmul(layer_2, weights['out']) + biases['out'] # Store layers weight & bias weights = { 'h1': w2v_utils.weight_variable(n_input, n_hidden_1, filename="weights_h1.txt"), 'h2': w2v_utils.weight_variable(n_hidden_1, n_hidden_2, filename="weights_h2.txt"), 'out': w2v_utils.weight_variable(n_hidden_2, n_classes, filename="weights_out.txt") } biases = { 'b1': w2v_utils.bias_variable([n_hidden_1], filename="biases_b1.txt"), 'b2': w2v_utils.bias_variable([n_hidden_2], filename="biases_b2.txt"), 'out': w2v_utils.bias_variable([n_classes], filename="biases_out.txt") } # Define loss and optimizer #learning rate # Optimizer: set up a variable that's incremented once per batch and # controls the learning rate decay. learning_rate = tf.train.exponential_decay( 0.02*0.01, # Base learning rate. #0.002 batch * batch_size, # Current index into the dataset. X_train.shape[0], # Decay step. 0.96, # Decay rate. staircase=True) # Construct model pred = tf.nn.relu(multilayer_perceptron(x, weights, biases)) #L2 regularization l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()]) #Softmax loss cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) #Total_cost cost = cost+ (regualarization*0.5*l2_loss) # Adam Optimizer optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost,global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Initializing the variables init = tf.initialize_all_variables() print "Network Initialized!" ERROR DETAILS 
